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Research methods--quantitative, qualitative, and more: overview.

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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
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• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

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Scientific Method

Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science ). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).

Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.

While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.

The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.

1. Overview and organizing themes

2. historical review: aristotle to mill, 3.1 logical constructionism and operationalism, 3.2. h-d as a logic of confirmation, 3.3. popper and falsificationism, 3.4 meta-methodology and the end of method, 4. statistical methods for hypothesis testing, 5.1 creative and exploratory practices.

• 5.2 Computer methods and the ‘new ways’ of doing science

6.1 “The scientific method” in science education and as seen by scientists

6.2 privileged methods and ‘gold standards’, 6.3 scientific method in the court room, 6.4 deviating practices, 7. conclusion, other internet resources, related entries.

This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.

The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.

Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.

Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.

Section 3 turns to 20 th century debates on scientific method. In the second half of the 20 th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20 th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.

In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.

As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.

Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences. [ 1 ]

We begin with a point made by Laudan (1968) in his historical survey of scientific method:

Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)

To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science ).

Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible ( The Republic , 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature ( Metaphysics Z , in Barnes 1984).

Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics , Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science ( epistêmê ) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality ).

In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon . This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/​synthesis, non-ampliative/​ampliative, or even confirmation/​verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.

The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application. [ 2 ] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.

During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16 th –18 th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists ; Boyle ; Henry More ; Galileo ).

In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.

Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16 th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon ).

It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks , this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia . [ 3 ] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World ( Principia , Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy .)

To his list of methodological prescriptions should be added Newton’s famous phrase “ hypotheses non fingo ” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton , Leibniz , Descartes , Boyle , Hume , enlightenment , as well as Shank 2008 for a historical overview.)

Not all 18 th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley ; David Hume ; Hume’s Newtonianism and Anti-Newtonianism ). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.

The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19 th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell .)

Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20 th century (see section 3 ).

Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors ( System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).

3. Logic of method and critical responses

The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.

Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20 th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.

Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)

Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4 . [ 4 ]

Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science .) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.

The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod (1924) and others in the 20 th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation ). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.

Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. )

Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.

Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.

A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.

The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).

From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.

Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:

History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)

The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle .) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.

The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.

An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place

Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).

An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology .) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.

A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results .)

By the close of the 20 th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.

Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.

Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid-19 th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce ).

These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.

Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.

In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation .

5. Method in Practice

Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.

A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century (see section 2 ) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery ). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.

Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that

creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)

Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is

the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)

Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.

Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa , exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.

The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).

5.2 Computer methods and ‘new ways’ of doing science

The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?

Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.

The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.

A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/​simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.

For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science ).

In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data .

6. Discourse on scientific method

Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science ) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.

One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002). [ 5 ] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)

Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of

(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)

Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.

Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures. [ 6 ] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how

The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)

Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)

Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.

Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.

In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).

Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that

ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)

But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).

The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as

fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community . (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)

However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it

wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)

This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).

The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.

One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17 th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19 th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20 th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.

Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.

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• Blackmun opinion , in Daubert v. Merrell Dow Pharmaceuticals (92–102), 509 U.S. 579 (1993).
• Scientific Method at philpapers. Darrell Rowbottom (ed.).
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What is the Scientific Method: How does it work and why is it important?

The scientific method is a systematic process involving steps like defining questions, forming hypotheses, conducting experiments, and analyzing data. It minimizes biases and enables replicable research, leading to groundbreaking discoveries like Einstein's theory of relativity, penicillin, and the structure of DNA. This ongoing approach promotes reason, evidence, and the pursuit of truth in science.

Updated on November 18, 2023

Beginning in elementary school, we are exposed to the scientific method and taught how to put it into practice. As a tool for learning, it prepares children to think logically and use reasoning when seeking answers to questions.

Rather than jumping to conclusions, the scientific method gives us a recipe for exploring the world through observation and trial and error. We use it regularly, sometimes knowingly in academics or research, and sometimes subconsciously in our daily lives.

In this article we will refresh our memories on the particulars of the scientific method, discussing where it comes from, which elements comprise it, and how it is put into practice. Then, we will consider the importance of the scientific method, who uses it and under what circumstances.

What is the scientific method?

The scientific method is a dynamic process that involves objectively investigating questions through observation and experimentation . Applicable to all scientific disciplines, this systematic approach to answering questions is more accurately described as a flexible set of principles than as a fixed series of steps.

The following representations of the scientific method illustrate how it can be both condensed into broad categories and also expanded to reveal more and more details of the process. These graphics capture the adaptability that makes this concept universally valuable as it is relevant and accessible not only across age groups and educational levels but also within various contexts.

Steps in the scientific method

While the scientific method is versatile in form and function, it encompasses a collection of principles that create a logical progression to the process of problem solving:

• Define a question : Constructing a clear and precise problem statement that identifies the main question or goal of the investigation is the first step. The wording must lend itself to experimentation by posing a question that is both testable and measurable.
• Gather information and resources : Researching the topic in question to find out what is already known and what types of related questions others are asking is the next step in this process. This background information is vital to gaining a full understanding of the subject and in determining the best design for experiments. 
• Form a hypothesis : Composing a concise statement that identifies specific variables and potential results, which can then be tested, is a crucial step that must be completed before any experimentation. An imperfection in the composition of a hypothesis can result in weaknesses to the entire design of an experiment.
• Perform the experiments : Testing the hypothesis by performing replicable experiments and collecting resultant data is another fundamental step of the scientific method. By controlling some elements of an experiment while purposely manipulating others, cause and effect relationships are established.
• Analyze the data : Interpreting the experimental process and results by recognizing trends in the data is a necessary step for comprehending its meaning and supporting the conclusions. Drawing inferences through this systematic process lends substantive evidence for either supporting or rejecting the hypothesis.
• Report the results : Sharing the outcomes of an experiment, through an essay, presentation, graphic, or journal article, is often regarded as a final step in this process. Detailing the project's design, methods, and results not only promotes transparency and replicability but also adds to the body of knowledge for future research.
• Retest the hypothesis : Repeating experiments to see if a hypothesis holds up in all cases is a step that is manifested through varying scenarios. Sometimes a researcher immediately checks their own work or replicates it at a future time, or another researcher will repeat the experiments to further test the hypothesis.

Where did the scientific method come from?

Oftentimes, ancient peoples attempted to answer questions about the unknown by:

• Making simple observations
• Discussing the possibilities with others deemed worthy of a debate
• Drawing conclusions based on dominant opinions and preexisting beliefs

For example, take Greek and Roman mythology. Myths were used to explain everything from the seasons and stars to the sun and death itself.

However, as societies began to grow through advancements in agriculture and language, ancient civilizations like Egypt and Babylonia shifted to a more rational analysis for understanding the natural world. They increasingly employed empirical methods of observation and experimentation that would one day evolve into the scientific method . 

In the 4th century, Aristotle, considered the Father of Science by many, suggested these elements , which closely resemble the contemporary scientific method, as part of his approach for conducting science:

• Study what others have written about the subject.
• Look for the general consensus about the subject.
• Perform a systematic study of everything even partially related to the topic.

By continuing to emphasize systematic observation and controlled experiments, scholars such as Al-Kindi and Ibn al-Haytham helped expand this concept throughout the Islamic Golden Age . 

In his 1620 treatise, Novum Organum , Sir Francis Bacon codified the scientific method, arguing not only that hypotheses must be tested through experiments but also that the results must be replicated to establish a truth. Coming at the height of the Scientific Revolution, this text made the scientific method accessible to European thinkers like Galileo and Isaac Newton who then put the method into practice.

As science modernized in the 19th century, the scientific method became more formalized, leading to significant breakthroughs in fields such as evolution and germ theory. Today, it continues to evolve, underpinning scientific progress in diverse areas like quantum mechanics, genetics, and artificial intelligence.

Why is the scientific method important?

The history of the scientific method illustrates how the concept developed out of a need to find objective answers to scientific questions by overcoming biases based on fear, religion, power, and cultural norms. This still holds true today.

By implementing this standardized approach to conducting experiments, the impacts of researchers’ personal opinions and preconceived notions are minimized. The organized manner of the scientific method prevents these and other mistakes while promoting the replicability and transparency necessary for solid scientific research.

The importance of the scientific method is best observed through its successes, for example: 

• “ Albert Einstein stands out among modern physicists as the scientist who not only formulated a theory of revolutionary significance but also had the genius to reflect in a conscious and technical way on the scientific method he was using.” Devising a hypothesis based on the prevailing understanding of Newtonian physics eventually led Einstein to devise the theory of general relativity .
• Howard Florey “Perhaps the most useful lesson which has come out of the work on penicillin has been the demonstration that success in this field depends on the development and coordinated use of technical methods.” After discovering a mold that prevented the growth of Staphylococcus bacteria, Dr. Alexander Flemimg designed experiments to identify and reproduce it in the lab, thus leading to the development of penicillin .
• James D. Watson “Every time you understand something, religion becomes less likely. Only with the discovery of the double helix and the ensuing genetic revolution have we had grounds for thinking that the powers held traditionally to be the exclusive property of the gods might one day be ours. . . .” By using wire models to conceive a structure for DNA, Watson and Crick crafted a hypothesis for testing combinations of amino acids, X-ray diffraction images, and the current research in atomic physics, resulting in the discovery of DNA’s double helix structure .

Final thoughts

As the cases exemplify, the scientific method is never truly completed, but rather started and restarted. It gave these researchers a structured process that was easily replicated, modified, and built upon. 

While the scientific method may “end” in one context, it never literally ends. When a hypothesis, design, methods, and experiments are revisited, the scientific method simply picks up where it left off. Each time a researcher builds upon previous knowledge, the scientific method is restored with the pieces of past efforts.

By guiding researchers towards objective results based on transparency and reproducibility, the scientific method acts as a defense against bias, superstition, and preconceived notions. As we embrace the scientific method's enduring principles, we ensure that our quest for knowledge remains firmly rooted in reason, evidence, and the pursuit of truth.

The AJE Team

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Home » Scientific Research – Types, Purpose and Guide

Scientific Research – Types, Purpose and Guide

Table of Contents

Scientific Research

Definition:

Scientific research is the systematic and empirical investigation of phenomena, theories, or hypotheses, using various methods and techniques in order to acquire new knowledge or to validate existing knowledge.

It involves the collection, analysis, interpretation, and presentation of data, as well as the formulation and testing of hypotheses. Scientific research can be conducted in various fields, such as natural sciences, social sciences, and engineering, and may involve experiments, observations, surveys, or other forms of data collection. The goal of scientific research is to advance knowledge, improve understanding, and contribute to the development of solutions to practical problems.

Types of Scientific Research

There are different types of scientific research, which can be classified based on their purpose, method, and application. In this response, we will discuss the four main types of scientific research.

Descriptive Research

Descriptive research aims to describe or document a particular phenomenon or situation, without altering it in any way. This type of research is usually done through observation, surveys, or case studies. Descriptive research is useful in generating ideas, understanding complex phenomena, and providing a foundation for future research. However, it does not provide explanations or causal relationships between variables.

Exploratory Research

Exploratory research aims to explore a new area of inquiry or develop initial ideas for future research. This type of research is usually conducted through observation, interviews, or focus groups. Exploratory research is useful in generating hypotheses, identifying research questions, and determining the feasibility of a larger study. However, it does not provide conclusive evidence or establish cause-and-effect relationships.

Experimental Research

Experimental research aims to test cause-and-effect relationships between variables by manipulating one variable and observing the effects on another variable. This type of research involves the use of an experimental group, which receives a treatment, and a control group, which does not receive the treatment. Experimental research is useful in establishing causal relationships, replicating results, and controlling extraneous variables. However, it may not be feasible or ethical to manipulate certain variables in some contexts.

Correlational Research

Correlational research aims to examine the relationship between two or more variables without manipulating them. This type of research involves the use of statistical techniques to determine the strength and direction of the relationship between variables. Correlational research is useful in identifying patterns, predicting outcomes, and testing theories. However, it does not establish causation or control for confounding variables.

Scientific Research Methods

Scientific research methods are used in scientific research to investigate phenomena, acquire knowledge, and answer questions using empirical evidence. Here are some commonly used scientific research methods:

Observational Studies

This method involves observing and recording phenomena as they occur in their natural setting. It can be done through direct observation or by using tools such as cameras, microscopes, or sensors.

Experimental Studies

This method involves manipulating one or more variables to determine the effect on the outcome. This type of study is often used to establish cause-and-effect relationships.

Survey Research

This method involves collecting data from a large number of people by asking them a set of standardized questions. Surveys can be conducted in person, over the phone, or online.

Case Studies

This method involves in-depth analysis of a single individual, group, or organization. Case studies are often used to gain insights into complex or unusual phenomena.

Meta-analysis

This method involves combining data from multiple studies to arrive at a more reliable conclusion. This technique can be used to identify patterns and trends across a large number of studies.

Qualitative Research

This method involves collecting and analyzing non-numerical data, such as interviews, focus groups, or observations. This type of research is often used to explore complex phenomena and to gain an understanding of people’s experiences and perspectives.

Quantitative Research

This method involves collecting and analyzing numerical data using statistical techniques. This type of research is often used to test hypotheses and to establish cause-and-effect relationships.

Longitudinal Studies

This method involves following a group of individuals over a period of time to observe changes and to identify patterns and trends. This type of study can be used to investigate the long-term effects of a particular intervention or exposure.

Data Analysis Methods

There are many different data analysis methods used in scientific research, and the choice of method depends on the type of data being collected and the research question. Here are some commonly used data analysis methods:

• Descriptive statistics: This involves using summary statistics such as mean, median, mode, standard deviation, and range to describe the basic features of the data.
• Inferential statistics: This involves using statistical tests to make inferences about a population based on a sample of data. Examples of inferential statistics include t-tests, ANOVA, and regression analysis.
• Qualitative analysis: This involves analyzing non-numerical data such as interviews, focus groups, and observations. Qualitative analysis may involve identifying themes, patterns, or categories in the data.
• Content analysis: This involves analyzing the content of written or visual materials such as articles, speeches, or images. Content analysis may involve identifying themes, patterns, or categories in the content.
• Data mining: This involves using automated methods to analyze large datasets to identify patterns, trends, or relationships in the data.
• Machine learning: This involves using algorithms to analyze data and make predictions or classifications based on the patterns identified in the data.

Application of Scientific Research

Scientific research has numerous applications in many fields, including:

• Medicine and healthcare: Scientific research is used to develop new drugs, medical treatments, and vaccines. It is also used to understand the causes and risk factors of diseases, as well as to develop new diagnostic tools and medical devices.
• Agriculture : Scientific research is used to develop new crop varieties, to improve crop yields, and to develop more sustainable farming practices.
• Technology and engineering : Scientific research is used to develop new technologies and engineering solutions, such as renewable energy systems, new materials, and advanced manufacturing techniques.
• Environmental science : Scientific research is used to understand the impacts of human activity on the environment and to develop solutions for mitigating those impacts. It is also used to monitor and manage natural resources, such as water and air quality.
• Education : Scientific research is used to develop new teaching methods and educational materials, as well as to understand how people learn and develop.
• Business and economics: Scientific research is used to understand consumer behavior, to develop new products and services, and to analyze economic trends and policies.
• Social sciences : Scientific research is used to understand human behavior, attitudes, and social dynamics. It is also used to develop interventions to improve social welfare and to inform public policy.

How to Conduct Scientific Research

Conducting scientific research involves several steps, including:

• Identify a research question: Start by identifying a question or problem that you want to investigate. This question should be clear, specific, and relevant to your field of study.
• Conduct a literature review: Before starting your research, conduct a thorough review of existing research in your field. This will help you identify gaps in knowledge and develop hypotheses or research questions.
• Develop a research plan: Once you have a research question, develop a plan for how you will collect and analyze data to answer that question. This plan should include a detailed methodology, a timeline, and a budget.
• Collect data: Depending on your research question and methodology, you may collect data through surveys, experiments, observations, or other methods.
• Analyze data: Once you have collected your data, analyze it using appropriate statistical or qualitative methods. This will help you draw conclusions about your research question.
• Interpret results: Based on your analysis, interpret your results and draw conclusions about your research question. Discuss any limitations or implications of your findings.
• Communicate results: Finally, communicate your findings to others in your field through presentations, publications, or other means.

Purpose of Scientific Research

The purpose of scientific research is to systematically investigate phenomena, acquire new knowledge, and advance our understanding of the world around us. Scientific research has several key goals, including:

• Exploring the unknown: Scientific research is often driven by curiosity and the desire to explore uncharted territory. Scientists investigate phenomena that are not well understood, in order to discover new insights and develop new theories.
• Testing hypotheses: Scientific research involves developing hypotheses or research questions, and then testing them through observation and experimentation. This allows scientists to evaluate the validity of their ideas and refine their understanding of the phenomena they are studying.
• Solving problems: Scientific research is often motivated by the desire to solve practical problems or address real-world challenges. For example, researchers may investigate the causes of a disease in order to develop new treatments, or explore ways to make renewable energy more affordable and accessible.
• Advancing knowledge: Scientific research is a collective effort to advance our understanding of the world around us. By building on existing knowledge and developing new insights, scientists contribute to a growing body of knowledge that can be used to inform decision-making, solve problems, and improve our lives.

Examples of Scientific Research

Here are some examples of scientific research that are currently ongoing or have recently been completed:

• Clinical trials for new treatments: Scientific research in the medical field often involves clinical trials to test new treatments for diseases and conditions. For example, clinical trials may be conducted to evaluate the safety and efficacy of new drugs or medical devices.
• Genomics research: Scientists are conducting research to better understand the human genome and its role in health and disease. This includes research on genetic mutations that can cause diseases such as cancer, as well as the development of personalized medicine based on an individual’s genetic makeup.
• Climate change: Scientific research is being conducted to understand the causes and impacts of climate change, as well as to develop solutions for mitigating its effects. This includes research on renewable energy technologies, carbon capture and storage, and sustainable land use practices.
• Neuroscience : Scientists are conducting research to understand the workings of the brain and the nervous system, with the goal of developing new treatments for neurological disorders such as Alzheimer’s disease and Parkinson’s disease.
• Artificial intelligence: Researchers are working to develop new algorithms and technologies to improve the capabilities of artificial intelligence systems. This includes research on machine learning, computer vision, and natural language processing.
• Space exploration: Scientific research is being conducted to explore the cosmos and learn more about the origins of the universe. This includes research on exoplanets, black holes, and the search for extraterrestrial life.

When to use Scientific Research

Some specific situations where scientific research may be particularly useful include:

• Solving problems: Scientific research can be used to investigate practical problems or address real-world challenges. For example, scientists may investigate the causes of a disease in order to develop new treatments, or explore ways to make renewable energy more affordable and accessible.
• Decision-making: Scientific research can provide evidence-based information to inform decision-making. For example, policymakers may use scientific research to evaluate the effectiveness of different policy options or to make decisions about public health and safety.
• Innovation : Scientific research can be used to develop new technologies, products, and processes. For example, research on materials science can lead to the development of new materials with unique properties that can be used in a range of applications.
• Knowledge creation : Scientific research is an important way of generating new knowledge and advancing our understanding of the world around us. This can lead to new theories, insights, and discoveries that can benefit society.

Advantages of Scientific Research

There are many advantages of scientific research, including:

• Improved understanding : Scientific research allows us to gain a deeper understanding of the world around us, from the smallest subatomic particles to the largest celestial bodies.
• Evidence-based decision making: Scientific research provides evidence-based information that can inform decision-making in many fields, from public policy to medicine.
• Technological advancements: Scientific research drives technological advancements in fields such as medicine, engineering, and materials science. These advancements can improve quality of life, increase efficiency, and reduce costs.
• New discoveries: Scientific research can lead to new discoveries and breakthroughs that can advance our knowledge in many fields. These discoveries can lead to new theories, technologies, and products.
• Economic benefits : Scientific research can stimulate economic growth by creating new industries and jobs, and by generating new technologies and products.
• Improved health outcomes: Scientific research can lead to the development of new medical treatments and technologies that can improve health outcomes and quality of life for people around the world.
• Increased innovation: Scientific research encourages innovation by promoting collaboration, creativity, and curiosity. This can lead to new and unexpected discoveries that can benefit society.

Limitations of Scientific Research

Scientific research has some limitations that researchers should be aware of. These limitations can include:

• Research design limitations : The design of a research study can impact the reliability and validity of the results. Poorly designed studies can lead to inaccurate or inconclusive results. Researchers must carefully consider the study design to ensure that it is appropriate for the research question and the population being studied.
• Sample size limitations: The size of the sample being studied can impact the generalizability of the results. Small sample sizes may not be representative of the larger population, and may lead to incorrect conclusions.
• Time and resource limitations: Scientific research can be costly and time-consuming. Researchers may not have the resources necessary to conduct a large-scale study, or may not have sufficient time to complete a study with appropriate controls and analysis.
• Ethical limitations : Certain types of research may raise ethical concerns, such as studies involving human or animal subjects. Ethical concerns may limit the scope of the research that can be conducted, or require additional protocols and procedures to ensure the safety and well-being of participants.
• Limitations of technology: Technology may limit the types of research that can be conducted, or the accuracy of the data collected. For example, certain types of research may require advanced technology that is not yet available, or may be limited by the accuracy of current measurement tools.
• Limitations of existing knowledge: Existing knowledge may limit the types of research that can be conducted. For example, if there is limited knowledge in a particular field, it may be difficult to design a study that can provide meaningful results.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Video transcript

Scientific Method

Illustration by J.R. Bee. ThoughtCo. 

• Cell Biology
• Weather & Climate
• B.A., Biology, Emory University
• A.S., Nursing, Chattahoochee Technical College

The scientific method is a series of steps followed by scientific investigators to answer specific questions about the natural world. It involves making observations, formulating a hypothesis , and conducting scientific experiments . Scientific inquiry starts with an observation followed by the formulation of a question about what has been observed. The steps of the scientific method are as follows:

Observation

The first step of the scientific method involves making an observation about something that interests you. This is very important if you are doing a science project because you want your project to be focused on something that will hold your attention. Your observation can be on anything from plant movement to animal behavior, as long as it is something you really want to know more about.​ This is where you come up with the idea for your science project.

Once you've made your observation, you must formulate a question about what you have observed. Your question should tell what it is that you are trying to discover or accomplish in your experiment. When stating your question you should be as specific as possible.​ For example, if you are doing a project on plants , you may want to know how plants interact with microbes. Your question may be: Do plant spices inhibit bacterial growth ?

The hypothesis is a key component of the scientific process. A hypothesis is an idea that is suggested as an explanation for a natural event, a particular experience, or a specific condition that can be tested through definable experimentation. It states the purpose of your experiment, the variables used, and the predicted outcome of your experiment. It is important to note that a hypothesis must be testable. That means that you should be able to test your hypothesis through experimentation .​ Your hypothesis must either be supported or falsified by your experiment. An example of a good hypothesis is: If there is a relation between listening to music and heart rate, then listening to music will cause a person's resting heart rate to either increase or decrease.

Once you've developed a hypothesis, you must design and conduct an experiment that will test it. You should develop a procedure that states very clearly how you plan to conduct your experiment. It is important that you include and identify a controlled variable or dependent variable in your procedure. Controls allow us to test a single variable in an experiment because they are unchanged. We can then make observations and comparisons between our controls and our independent variables (things that change in the experiment) to develop an accurate conclusion.​

The results are where you report what happened in the experiment. That includes detailing all observations and data made during your experiment. Most people find it easier to visualize the data by charting or graphing the information.​

The final step of the scientific method is developing a conclusion. This is where all of the results from the experiment are analyzed and a determination is reached about the hypothesis. Did the experiment support or reject your hypothesis? If your hypothesis was supported, great. If not, repeat the experiment or think of ways to improve your procedure.

• Null Hypothesis Examples
• Examples of Independent and Dependent Variables
• The 10 Most Important Lab Safety Rules
• Difference Between Independent and Dependent Variables
• Six Steps of the Scientific Method
• Scientific Method Flow Chart
• What Is an Experiment? Definition and Design
• Scientific Method Lesson Plan
• How To Design a Science Fair Experiment
• Science Projects for Every Subject
• How to Do a Science Fair Project
• What Are the Elements of a Good Hypothesis?
• How to Write a Lab Report
• What Is a Hypothesis? (Science)
• Understanding Simple vs Controlled Experiments
• Biology Science Fair Project Ideas

What Are The Steps Of The Scientific Method?

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Science is not just knowledge. It is also a method for obtaining knowledge. Scientific understanding is organized into theories.

The scientific method is a step-by-step process used by researchers and scientists to determine if there is a relationship between two or more variables. Psychologists use this method to conduct psychological research, gather data, process information, and describe behaviors.

It involves careful observation, asking questions, formulating hypotheses, experimental testing, and refining hypotheses based on experimental findings.

How it is Used

The scientific method can be applied broadly in science across many different fields, such as chemistry, physics, geology, and psychology. In a typical application of this process, a researcher will develop a hypothesis, test this hypothesis, and then modify the hypothesis based on the outcomes of the experiment.

The process is then repeated with the modified hypothesis until the results align with the observed phenomena. Detailed steps of the scientific method are described below.

Keep in mind that the scientific method does not have to follow this fixed sequence of steps; rather, these steps represent a set of general principles or guidelines.

7 Steps of the Scientific Method

Psychology uses an empirical approach.

Empiricism (founded by John Locke) states that the only source of knowledge comes through our senses – e.g., sight, hearing, touch, etc.

Empirical evidence does not rely on argument or belief. Thus, empiricism is the view that all knowledge is based on or may come from direct observation and experience.

The empiricist approach of gaining knowledge through experience quickly became the scientific approach and greatly influenced the development of physics and chemistry in the 17th and 18th centuries.

Step 1: Make an Observation (Theory Construction)

Every researcher starts at the very beginning. Before diving in and exploring something, one must first determine what they will study – it seems simple enough!

By making observations, researchers can establish an area of interest. Once this topic of study has been chosen, a researcher should review existing literature to gain insight into what has already been tested and determine what questions remain unanswered.

This assessment will provide helpful information about what has already been comprehended about the specific topic and what questions remain, and if one can go and answer them.

Specifically, a literature review might implicate examining a substantial amount of documented material from academic journals to books dating back decades. The most appropriate information gathered by the researcher will be shown in the introduction section or abstract of the published study results.

The background material and knowledge will help the researcher with the first significant step in conducting a psychology study, which is formulating a research question.

This is the inductive phase of the scientific process. Observations yield information that is used to formulate theories as explanations. A theory is a well-developed set of ideas that propose an explanation for observed phenomena.

Inductive reasoning moves from specific premises to a general conclusion. It starts with observations of phenomena in the natural world and derives a general law.

Step 2: Ask a Question

Once a researcher has made observations and conducted background research, the next step is to ask a scientific question. A scientific question must be defined, testable, and measurable.

A useful approach to develop a scientific question is: “What is the effect of…?” or “How does X affect Y?”

To answer an experimental question, a researcher must identify two variables: the independent and dependent variables.

The independent variable is the variable manipulated (the cause), and the dependent variable is the variable being measured (the effect).

An example of a research question could be, “Is handwriting or typing more effective for retaining information?” Answering the research question and proposing a relationship between the two variables is discussed in the next step.

Step 3: Form a Hypothesis (Make Predictions)

A hypothesis is an educated guess about the relationship between two or more variables. A hypothesis is an attempt to answer your research question based on prior observation and background research. Theories tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.

For example, a researcher might ask about the connection between sleep and educational performance. Do students who get less sleep perform worse on tests at school?

It is crucial to think about different questions one might have about a particular topic to formulate a reasonable hypothesis. It would help if one also considered how one could investigate the causalities.

It is important that the hypothesis is both testable against reality and falsifiable. This means that it can be tested through an experiment and can be proven wrong.

The falsification principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory to be considered scientific, it must be able to be tested and conceivably proven false.

To test a hypothesis, we first assume that there is no difference between the populations from which the samples were taken. This is known as the null hypothesis and predicts that the independent variable will not influence the dependent variable.

Examples of “if…then…” Hypotheses:

• If one gets less than 6 hours of sleep, then one will do worse on tests than if one obtains more rest.
• If one drinks lots of water before going to bed, one will have to use the bathroom often at night.
• If one practices exercising and lighting weights, then one’s body will begin to build muscle.

The research hypothesis is often called the alternative hypothesis and predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Although one could state and write a scientific hypothesis in many ways, hypotheses are usually built like “if…then…” statements.

Step 4: Run an Experiment (Gather Data)

The next step in the scientific method is to test your hypothesis and collect data. A researcher will design an experiment to test the hypothesis and gather data that will either support or refute the hypothesis.

The exact research methods used to examine a hypothesis depend on what is being studied. A psychologist might utilize two primary forms of research, experimental research, and descriptive research.

The scientific method is objective in that researchers do not let preconceived ideas or biases influence the collection of data and is systematic in that experiments are conducted in a logical way.

Experimental Research

Experimental research is used to investigate cause-and-effect associations between two or more variables. This type of research systematically controls an independent variable and measures its effect on a specified dependent variable.

Experimental research involves manipulating an independent variable and measuring the effect(s) on the dependent variable. Repeating the experiment multiple times is important to confirm that your results are accurate and consistent.

One of the significant advantages of this method is that it permits researchers to determine if changes in one variable cause shifts in each other.

While experiments in psychology typically have many moving parts (and can be relatively complex), an easy investigation is rather fundamental. Still, it does allow researchers to specify cause-and-effect associations between variables.

Most simple experiments use a control group, which involves those who do not receive the treatment, and an experimental group, which involves those who do receive the treatment.

An example of experimental research would be when a pharmaceutical company wants to test a new drug. They give one group a placebo (control group) and the other the actual pill (experimental group).

Descriptive Research

Descriptive research is generally used when it is challenging or even impossible to control the variables in question. Examples of descriptive analysis include naturalistic observation, case studies , and correlation studies .

One example of descriptive research includes phone surveys that marketers often use. While they typically do not allow researchers to identify cause and effect, correlational studies are quite common in psychology research. They make it possible to spot associations between distinct variables and measure the solidity of those relationships.

Step 5: Analyze the Data and Draw Conclusions

Once a researcher has designed and done the investigation and collected sufficient data, it is time to inspect this gathered information and judge what has been found. Researchers can summarize the data, interpret the results, and draw conclusions based on this evidence using analyses and statistics.

Upon completion of the experiment, you can collect your measurements and analyze the data using statistics. Based on the outcomes, you will either reject or confirm your hypothesis.

Analyze the Data

So, how does a researcher determine what the results of their study mean? Statistical analysis can either support or refute a researcher’s hypothesis and can also be used to determine if the conclusions are statistically significant.

When outcomes are said to be “statistically significant,” it is improbable that these results are due to luck or chance. Based on these observations, investigators must then determine what the results mean.

An experiment will support a hypothesis in some circumstances, but sometimes it fails to be truthful in other cases.

What occurs if the developments of a psychology investigation do not endorse the researcher’s hypothesis? It does mean that the study was worthless. Simply because the findings fail to defend the researcher’s hypothesis does not mean that the examination is not helpful or instructive.

This kind of research plays a vital role in supporting scientists in developing unexplored questions and hypotheses to investigate in the future. After decisions have been made, the next step is to communicate the results with the rest of the scientific community.

This is an integral part of the process because it contributes to the general knowledge base and can assist other scientists in finding new research routes to explore.

If the hypothesis is not supported, a researcher should acknowledge the experiment’s results, formulate a new hypothesis, and develop a new experiment.

We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist that could refute a theory.

Draw Conclusions and Interpret the Data

When the empirical observations disagree with the hypothesis, a number of possibilities must be considered. It might be that the theory is incorrect, in which case it needs altering, so it fully explains the data.

Alternatively, it might be that the hypothesis was poorly derived from the original theory, in which case the scientists were expecting the wrong thing to happen.

It might also be that the research was poorly conducted, or used an inappropriate method, or there were factors in play that the researchers did not consider. This will begin the process of the scientific method again.

If the hypothesis is supported, the researcher can find more evidence to support their hypothesis or look for counter-evidence to strengthen their hypothesis further.

In either scenario, the researcher should share their results with the greater scientific community.

Step 6: Share Your Results

One of the final stages of the research cycle involves the publication of the research. Once the report is written, the researcher(s) may submit the work for publication in an appropriate journal.

Usually, this is done by writing up a study description and publishing the article in a professional or academic journal. The studies and conclusions of psychological work can be seen in peer-reviewed journals such as  Developmental Psychology , Psychological Bulletin, the  Journal of Social Psychology, and numerous others.

Scientists should report their findings by writing up a description of their study and any subsequent findings. This enables other researchers to build upon the present research or replicate the results.

As outlined by the American Psychological Association (APA), there is a typical structure of a journal article that follows a specified format. In these articles, researchers:

• Supply a brief narrative and background on previous research
• Give their hypothesis
• Specify who participated in the study and how they were chosen
• Provide operational definitions for each variable
• Explain the measures and methods used to collect data
• Describe how the data collected was interpreted
• Discuss what the outcomes mean

A detailed record of psychological studies and all scientific studies is vital to clearly explain the steps and procedures used throughout the study. So that other researchers can try this experiment too and replicate the results.

The editorial process utilized by academic and professional journals guarantees that each submitted article undergoes a thorough peer review to help assure that the study is scientifically sound. Once published, the investigation becomes another piece of the current puzzle of our knowledge “base” on that subject.

This last step is important because all results, whether they supported or did not support the hypothesis, can contribute to the scientific community. Publication of empirical observations leads to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular.

The editorial process utilized by academic and professional journals guarantees that each submitted article undergoes a thorough peer review to help assure that the study is scientifically sound.

Once published, the investigation becomes another piece of the current puzzle of our knowledge “base” on that subject.

By replicating studies, psychologists can reduce errors, validate theories, and gain a stronger understanding of a particular topic.

Step 7: Repeat the Scientific Method (Iteration)

Now, if one’s hypothesis turns out to be accurate, find more evidence or find counter-evidence. If one’s hypothesis is false, create a new hypothesis or try again.

One may wish to revise their first hypothesis to make a more niche experiment to design or a different specific question to test.

The amazingness of the scientific method is that it is a comprehensive and straightforward process that scientists, and everyone, can utilize over and over again.

So, draw conclusions and repeat because the scientific method is never-ending, and no result is ever considered perfect.

The scientific method is a process of:

• Making an observation.
• Forming a hypothesis.
• Making a prediction.
• Experimenting to test the hypothesis.

The procedure of repeating the scientific method is crucial to science and all fields of human knowledge.

Further Information

• Karl Popper – Falsification
• Thomas – Kuhn Paradigm Shift
• Positivism in Sociology: Definition, Theory & Examples
• Is Psychology a Science?
• Psychology as a Science (PDF)

List the 6 steps of the scientific methods in order

• Make an observation (theory construction)
• Ask a question. A scientific question must be defined, testable, and measurable.
• Form a hypothesis (make predictions)
• Run an experiment to test the hypothesis (gather data)
• Analyze the data and draw conclusions
• Share your results so that other researchers can make new hypotheses

What is the first step of the scientific method?

The first step of the scientific method is making an observation. This involves noticing and describing a phenomenon or group of phenomena that one finds interesting and wishes to explain.

Observations can occur in a natural setting or within the confines of a laboratory. The key point is that the observation provides the initial question or problem that the rest of the scientific method seeks to answer or solve.

What is the scientific method?

The scientific method is a step-by-step process that investigators can follow to determine if there is a causal connection between two or more variables.

Psychologists and other scientists regularly suggest motivations for human behavior. On a more casual level, people judge other people’s intentions, incentives, and actions daily.

While our standard assessments of human behavior are subjective and anecdotal, researchers use the scientific method to study psychology objectively and systematically.

All utilize a scientific method to study distinct aspects of people’s thinking and behavior. This process allows scientists to analyze and understand various psychological phenomena, but it also provides investigators and others a way to disseminate and debate the results of their studies.

The outcomes of these studies are often noted in popular media, which leads numerous to think about how or why researchers came to the findings they did.

Why Use the Six Steps of the Scientific Method

The goal of scientists is to understand better the world that surrounds us. Scientific research is the most critical tool for navigating and learning about our complex world.

Without it, we would be compelled to rely solely on intuition, other people’s power, and luck. We can eliminate our preconceived concepts and superstitions through methodical scientific research and gain an objective sense of ourselves and our world.

All psychological studies aim to explain, predict, and even control or impact mental behaviors or processes. So, psychologists use and repeat the scientific method (and its six steps) to perform and record essential psychological research.

So, psychologists focus on understanding behavior and the cognitive (mental) and physiological (body) processes underlying behavior.

In the real world, people use to understand the behavior of others, such as intuition and personal experience. The hallmark of scientific research is evidence to support a claim.

Scientific knowledge is empirical, meaning it is grounded in objective, tangible evidence that can be observed repeatedly, regardless of who is watching.

The scientific method is crucial because it minimizes the impact of bias or prejudice on the experimenter. Regardless of how hard one tries, even the best-intentioned scientists can’t escape discrimination. can’t

It stems from personal opinions and cultural beliefs, meaning any mortal filters data based on one’s experience. Sadly, this “filtering” process can cause a scientist to favor one outcome over another.

For an everyday person trying to solve a minor issue at home or work, succumbing to these biases is not such a big deal; in fact, most times, it is important.

But in the scientific community, where results must be inspected and reproduced, bias or discrimination must be avoided.

When to Use the Six Steps of the Scientific Method ?

One can use the scientific method anytime, anywhere! From the smallest conundrum to solving global problems, it is a process that can be applied to any science and any investigation.

Even if you are not considered a “scientist,” you will be surprised to know that people of all disciplines use it for all kinds of dilemmas.

Try to catch yourself next time you come by a question and see how you subconsciously or consciously use the scientific method.

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How to Conduct Scientific Research?

United Nations Educational, Scientific and Cultural Organization (UNESCO) defines research as systematic and creative actions taken to increase knowledge about humans, culture, and society and to apply it in new areas of interest. Scientific research is the research performed by applying systematic and constructed scientific methods to obtain, analyze, and interpret data.

Scientific research is the neutral, systematic, planned, and multiple-step process that uses previously discovered facts to advance knowledge that does not exist in the literature. It can be classified as observational or experimental with respect to data collection techniques, descriptive or analytical with respect to causality, and prospective, retrospective, or cross-sectional with respect to time ( 1 ).

All scientific investigations start with a specific research question and the formulation of a hypothesis to answer this question. Hypothesis should be clear, specific, and directly aim to answer the research question. A strong and testable hypothesis is the fundamental part of the scientific research. The next step is testing the hypothesis using scientific method to approve or disapprove it.

Scientific method should be neutral, objective, rational, and as a result, should be able to approve or disapprove the hypothesis. The research plan should include the procedure to obtain data and evaluate the variables. It should ensure that analyzable data are obtained. It should also include plans on the statistical analysis to be performed. The number of subjects and controls needed to get valid statistical results should be calculated, and data should be obtained in appropriate numbers and methods. The researcher should be continuously observing and recording all data obtained.

Data should be analyzed with the most appropriate statistical methods and be rearranged to make more sense if needed. Unfortunately, results obtained via analyses are not always sufficiently clear. Multiple reevaluations of data, review of the literature, and interpretation of results in light of previous research are required. Only after the completion of these stages can a research be written and presented to the scientific society. A well-conducted and precisely written research should always be open to scientific criticism. It should also be kept in mind that research should be in line with ethical rules all through its stages.

Actually, psychiatric research has been developing rapidly, possibly even more than any other medical field, thus reflecting the utilization of new research methods and advanced treatment technologies. Nevertheless, basic research principles and ethical considerations keep their importance.

Ethics are standards used to differentiate acceptable and unacceptable behavior. Adhering to ethical standards in scientific research is noteworthy because of many different reasons. First, these standards promote the aims of research, such as knowledge, truth, and avoidance of error. For example, prohibitions against fabricating, falsifying, or misrepresenting research data promote truth and minimize error. In addition, ethical standards promote values that are essential to collaborative work, such as trust, accountability, mutual respect, and fairness. Many ethical standards in research, such as guidelines for authorship, copyright and patenting policies, data-sharing policies, and confidentiality rules in peer review, are designed to protect intellectual property interests while encouraging collaboration. Many ethical standards such as policies on research misconduct and conflicts of interest are necessary to ensure that researchers can be held accountable to the public. Last but not the least, ethical standards of research promote a variety of other important moral and social values, such as social responsibility, human rights, animal welfare, compliance with the law, and public health and safety ( 2 ). In conclusion, for the good of science and humanity, research has the inevitable responsibility of precisely transferring the knowledge to new generations ( 3 ).

In medical research, all clinical investigations are obliged to comply with some ethical principles. These principles could be summarized as respect to humans, respect to the society, benefit, harmlessness, autonomy, and justice. Respect to humans indicates that all humans have the right to refuse to participate in an investigation or to withdraw their consent any time without any repercussions. Respect to society indicates that clinical research should seek answers to scientific questions using scientific methods and should benefit the society. Benefit indicates that research outcomes are supposed to provide solutions to a health problem. Harmlessness describes all necessary precautions that are taken to protect volunteers from potential harm. Autonomy indicates that participating in research is voluntary and with freewill. Justice indicates that subject selection is based on justice and special care is taken for special groups that could be easily traumatized ( 4 ).

In psychiatric studies, if the patient is not capable of giving consent, the relatives have the right to consent on behalf of the patient. This is based on the idea of providing benefit to the patient with discovery of new treatment methods via research. However, the relatives’ consent rights are under debate from an ethical point of view. On the other hand, research on those patients aim to directly get new knowledge about them, and it looks like an inevitable necessity. The only precaution that could be taken to overcome this ambivalence has been the scrupulous audit of the Research Ethic Committees. Still, there are many examples that show that this method is not always able to prevent patient abuse ( 5 ). Therefore, it is difficult to claim autonomy when psychiatric patients are studied, and psychiatric patients are considered among patients to require special care.

We are proud to publish in our journal studies that overcome many burdens.

Scientific Method

1. the practice of science: an introduction to research methods.

When some people think of science, they think of formulas and facts to memorize. Many of us probably studied for a test in a science class by memorizing the names of the four nucleotides in DNA (adenine, cytosine, guanine, and thymine) or by practicing with one of Newton ‘s laws of motion, like f = ma (force equals mass times acceleration). While this knowledge is an important part of science, it is not all of science. In addition to a body of knowledge that includes formulas and facts, science is a practice by which we pursue answers to questions that can be approached scientifically. This practice is referred to collectively as scientific research , and while the techniques that scientists use to conduct research may differ between disciplines, the underlying principles and objectives are similar. Whether you are talking about biology, chemistry, geology, physics, or any other scientific field, the body of knowledge that is built through these disciplines is based on the collection of data that are then analyzed and interpreted in light of other research findings. How do we know about adenine, cytosine, guanine, and thymine? These were not revealed by chance, but through the work of many scientists collecting data, evaluating the results, and putting together a comprehensive theory that explained their observations .

A brief history of scientific practice

The recorded roots of formal scientific research lie in the collective work of a number of individuals in ancient Greek, Persian, Arab, Indian, Chinese, and European cultures, rather than from a single person or event. The Greek mathematician Pythagoras is regarded as the first person to promote a scientific hypothesis when, based on his descriptive study of the movement of stars in the sky in the 5 th century BCE , he proposed that the Earth was round. The Indian mathematician and astronomer Aryabhata used descriptive records regarding the movement of objects in the night sky to propose in the 6 th century CE that the sun was the center of the solar system. In the 9 th century, Chinese alchemists invented gunpowder while performing experiments attempting to make gold from other substances. And the Middle Eastern scientist Alhazen is credited with devising the concept of the scientific experiment while researching properties related to vision and light around 1000 CE.

These and other events demonstrate that a scientific approach to addressing questions about the natural world has long been present in many cultures. The roots of modern scientific research methods , however, are considered by many historians to lie in the Scientific Revolution that occurred in Europe in the 16 th and 17 th centuries. Most historians cite the beginning of the Scientific Revolution as the publication of De Revolutionibus Orbium Coelestium (On the Revolutions of the Heavenly Spheres) in 1543 by the Polish astronomer Nicolaus Copernicus . Copernicus’s careful observation and description of the movement of planets in relation to the Earth led him to hypothesize that the sun was the center of the solar system and the planets revolved around the sun in progressively larger orbits in the following order: Mercury, Venus, Earth, Mars, Jupiter, and Saturn (Figure 1). Though Copernicus was not the first person to propose a heliocentric view of the solar system, his systematic gathering of data provided a rigorous argument that challenged the commonly held belief that Earth was the center of the universe .

The Scientific Revolution was subsequently fueled by the work of Galileo Galilei , Johannes Kepler , Isaac Newton (Figure 2), and others, who not only challenged the traditional geocentric view of the universe , but explicitly rejected the older philosophical approaches to natural science popularized by Aristotle . A key event marking the rejection of the philosophical method was the publication of Novum Organum: New Directions Concerning the Interpretation of Nature by Francis Bacon in 1620. Bacon was not a scientist, but rather an English philosopher and essayist, and Novum is a work on logic. In it, Bacon presented an inductive method of reasoning that he argued was superior to the philosophical approach of Aristotle. The Baconian method involved a repeating cycle of observation , hypothesis , experimentation, and the need for independent verification. Bacon’s work championed a method that was objective, logical, and empirical and provided a basis for the development of scientific research methodology .

Bacon’s method of scientific reasoning was further refined by the publication of Philosophiæ Naturalis Principia Mathematica (Mathematical Principles of Natural Philosophy) by the English physicist and mathematician Isaac Newton in 1686. Principia established four rules (described in more detail here ) that have become the basis of modern approaches to science. In brief, Newton ‘s rules proposed that the simplest explanation of natural phenomena is often the best, countering the practice that was common in his day of assigning complicated explanations derived from belief systems , the occult, and observations of natural events. And Principia maintained that special explanations of new data should not be used when a reasonable explanation already exists, specifically criticizing the tendency of many of Newton ‘s contemporaries to embellish the significance of their findings with exotic new explanations.

Bacon and Newton laid the foundation that has been built upon by modern scientists and researchers in developing a rigorous methodology for investigating natural phenomena. In particular, the English statisticians Karl Pearson and Ronald Fisher significantly refined scientific research in the 20 th century by developing statistical techniques for data analysis and research design (see our Statistics in Science module). And the practice of science continues to evolve today, as new tools and technologies become available and our knowledge about the natural world grows. The practice of science is commonly misrepresented as a simple, four- or five-step path to answering a scientific question, called “The Scientific Method.” In reality, scientists rarely follow such a straightforward path through their research. Instead, scientific research includes many possible paths, not all of which lead to unequivocal answers. The real scientific method , or practice of science, is much more dynamic and interesting.

Comprehension Checkpoint

Scientific research, if done correctly, follows a straightforward five-step path and leads to definite answers.

More than one Scientific Method

The typical presentation of the Scientific Method (Figure 3) suggests that scientific research follows a linear path, proceeding from a question through observation , hypothesis formation, experimentation, and finally producing results and a conclusion. However, scientific research does not always proceed linearly. For example, prior to the mid 1800s, a popular scientific hypothesis held that maggots and microorganisms could be spontaneously generated from the inherent life-force that existed in some foods. Louis Pasteur doubted this hypothesis, and this led him to conduct a series of experiments that would eventually disprove the theory of spontaneous generation (see our Experimentation in Scientific Research module). Pasteur’s work would be difficult to characterize using Figure 3 – while it did involve experimentation, he did not develop a hypothesis prior to his experiments. Instead he was motivated to disprove an existing hypothesis. Or consider the work of Grove Karl Gilbert , who conducted research on the Henry Mountains in Utah in the late 1800s (see our Description in Scientific Research module). Gilbert was not drawn to the area by a pressing scientific question, but rather he was sent there by the US government to explore the region. Further, Gilbert did not perform a single experiment in the Henry Mountains; his work was based solely on observation and description, yet no one would dispute that Gilbert was practicing science. The traditional and simplistic Scientific Method presented in Figure 3 does not begin to reflect the richness or diversity of scientific research, let alone the diversity of scientists themselves.

Scientific research methods

Scientific research is a robust and dynamic practice that employs multiple methods toward investigating phenomena, including experimentation, description, comparison, and modeling. Though these methods are described separately both here and in more detail in subsequent modules, many of these methods overlap or are used in combination. For example, when NASA scientists purposefully slammed a 370 kg spacecraft named Deep Impact into a passing comet in 2005, the study had some aspects of descriptive research and some aspects of experimental research (see our Experimentation in Scientific Research module). Many scientific investigations largely employ one method, but different methods may be combined in a single study, or a single study may have characteristics of more than one method. The choice of which research method to use is personal and depends on the experiences of the scientists conducting the research and the nature of the question they are seeking to address. Despite the overlap and interconnectedness of these research methods, it is useful to discuss them separately to understand the principal characteristics of each and the ways they can be used to investigate a question.

Experimentation: Experimental methods are used to investigate the relationship(s) between two or more variables when at least one of those variables can be intentionally controlled or manipulated. The resulting effect of that manipulation (often called a treatment) can then be measured on another variable or variables. The work of the French scientist Louis Pasteur is a classic example. Pasteur put soup broth in a series of flasks, some open to the atmosphere and others sealed. He then measured the effect that the flask type had on the appearance of microorganisms in the soup broth in an effort to study the source of those microorganisms (see our Experimentation in Science module). Description: Description is used to gather data regarding natural phenomena and natural relationships and includes observations and measurements of behaviors. A classic example of a descriptive study is Copernicus’s observations and sketches of the movement of planets in the sky in an effort to determine if Earth or the sun is the orbital center of those objects (see our Description in Scientific Research module). Comparison: Comparison is used to determine and quantify relationships between two or more variables by observing different groups that either by choice or circumstance are exposed to different treatments. Examples of comparative research are the studies that were initiated in the 1950s to investigate the relationship between cigarette smoking and lung cancer in which scientists compared individuals who had chosen to smoke of their own accord with nonsmokers and correlated the decision to smoke (the treatment) with various health problems including lung cancer (see our Comparison in Scientific Research module). Modeling: Both physical and computer-based models are built to mimic natural systems and then used to conduct experiments or make observations. Weather forecasts are an example of scientific modeling that we see every day, where data collected on temperature, wind speed, and direction are used in combination with known physics of atmospheric circulation to predict the path of storms and other weather patterns (see our Modeling in Scientific Research module).

These methods are interconnected and are often used in combination to fully understand complex phenomenon. Modeling and experimentation are ways of simplifying systems toward understanding causality and future events. However, both rely on assumptions and knowledge of existing systems that can be provided by descriptive studies or other experiments . Description and comparison are used to understand existing systems and are used to examine the application of experimental and modeling results in real-world systems. Results from descriptive and comparative studies are often used to confirm causal relationships identified by models and experiments. While some questions lend themselves to one or another strategy due to the scope or nature of the problem under investigation, most areas of scientific research employ all of these methods as a means of complementing one another toward clarifying a specific hypothesis , theory , or idea in science.

Scientific research methods, such as experimentation , description , comparison , and modeling ,

• are interconnected and are often used in combination.
• are more effective if used alone.

Research methods in practice: The investigation of stratospheric ozone depletion

Scientific theories are clarified and strengthened through the collection of data from more than one method that generate multiple lines of evidence . Take, for example, the various research methods used to investigate what came to be known as the “ozone hole.”

Early descriptive and comparative studies point to problem: In 1957, the British Antarctic Survey (BAS) began a descriptive study of stratospheric ozone levels in an effort to better understand the role that ozone plays in absorbing solar energy (MacDowall & Sutcliffe, 1960). For the next 20 years, the BAS recorded ozone levels and observed seasonal shifts in ozone levels, which they attributed to natural fluctuations. In the mid-1970s, however, the BAS began to note a dramatic drop in ozone levels that they correlated with the change of seasons in the Antarctic. Within a decade, they noted that a seasonal “ozone hole” (Figure 4) had begun to appear over the South Pole (Farman et al., 1985).

The development of new technology opens novel research paths: Concurrent with the early BAS studies, the British scientist James Lovelock was working on developing new technology for the detection of trace concentrations of gases and vapors in the atmosphere (Lovelock, 1960). One instrument that Lovelock invented was a sensitive electron capture detector that could quantify atmospheric levels of chlorofluorocarbons (CFCs). At the time, CFCs were widely used as refrigerants and as propellants in aerosol cans and they were thought to be stable in the atmosphere and thus harmless chemicals. In 1970, Lovelock began an observational study of atmospheric CFCs and found that the chemicals were indeed very stable and could be carried long distances from major urban air pollution sources by prevailing winds. Under the impression that CFCs were chemically inert, Lovelock proposed that the chemicals could be used as benign atmospheric tracers of large air mass movements (Lovelock, 1971).

Modeling and experimental research are used to draw causal connections: In 1972, F. Sherwood Rowland, a chemist at the University of California at Irvine, attended a lecture on Lovelock’s work. Rowland became interested in CFCs and began studying the subject with a colleague at Irvine, Mario Molina. Molina and Rowland were familiar with modeling research by Paul Crutzen, a researcher at the National Center for Atmospheric Research in Colorado, that had previously shown that nitrogen oxides are involved in chemical reactions in the stratosphere and can influence upper atmosphere ozone levels (Crutzen, 1970). They were also familiar with modeling research by Harold Johnston, an atmospheric chemist at the University of California at Berkeley, which suggested that nitrogen oxide emissions from supersonic jets could reduce stratospheric ozone levels (Johnston, 1971). With these studies in mind, they consulted experimental research published by Michael Clyne and Ronald Walker, two British chemists, regarding the reaction rates of several chlorine-containing compounds (Clyne & Walker, 1973). In 1974, Molina and Rowland published a landmark study in the journal Nature in which they modeled chemical kinetics to show that CFCs were not completely inert, and that they could be transported to high altitudes where they would break apart in strong sunlight and release chlorine radicals (Molina & Rowland, 1974). Molina and Rowland’s model predicted that the chlorine radicals, which are reactive, would cause the destruction of significant amounts of ozone in the stratosphere.

Descriptive and comparative research provide real-world confirmation : In 1976, a group of scientists led by Allan Lazrus at the National Center for Atmospheric Research in Boulder, Colorado, used balloons to carry instruments aloft that could sample air at high altitudes. In these samples, they were able to detect the presence of CFCs above the troposphere – confirming that CFCs did indeed reach the stratosphere and that once there, they could decompose in light (Lazrus et al., 1976). Further research conducted using balloons and high-atmosphere aircraft in the 1980s confirmed that chlorine and chlorine oxide radicals contribute to the loss of ozone over the Antarctic (McElroy et al., 1986). By the late 1980s, scientists began to examine the possible link between ozone loss and skin cancer because high levels of ultraviolet light, as would exist under an ozone hole, can cause skin cancer. In areas such as Southern Chile, where the Antarctic ozone hole overlaps with a populated land mass, a significant correlation was indeed found between the growing ozone hole and increasing rates of skin cancer (Abarca & Casiccia, 2002).

As a result of this collection of diverse yet complementary scientific evidence , the world community began to limit the use of CFCs and ratified the Montreal Protocol in 1988, which imposed strict international limits on CFC use. In 1995, Molina, Rowland, and Crutzen shared the Nobel Prize in chemistry for their research that contributed to our understanding of ozone chemistry.

The ozone story (further detailed in our Resources for this module; see The Ozone Depletion Phenomenon under Research) highlights an important point: Scientific research is multi-dimensional, non-linear, and often leads down unexpected pathways. James Lovelock had no intention of contributing to the ozone depletion story; his work was directed at quantifying atmospheric CFC levels. Although gaining an understanding of the ozone hole may appear as a linear progression of events when viewed in hindsight, this was not the case at the time. While each researcher or research team built on previous work, it is more accurate to portray the relationships between their studies as a web of networked events, not as a linear series. Lovelock’s work led Molina and Rowland to their ozone depletion models , but Lovelock’s work is also widely cited by researchers developing improved electron capture detectors. Molina and Rowland not only used Lovelock’s work, but they drew on the research of Crutzen, Johnston, Clyne, Walker, and many others. Any single research advance was subsequently pursued in a number of different directions that complemented and reinforced one another – a common phenomenon in science. The entire ozone story required modeling, experiments , comparative research, and descriptive studies to develop a coherent theory about the role of ozone in the atmosphere , how we as humans are affecting it, and how we are also affected by it.

The ozone research story shows that, in practice, scientific research is

• a linear step-by-step procedure.
• an interconnected web of related studies.

The real practice of science

Scientific research methods are part of the practice through which questions can be addressed scientifically. These methods all produce data that are subject to analysis and interpretation and lead to ideas in science such as hypotheses , theories , and laws . Scientific ideas are developed and disseminated through the literature, where individuals and groups may debate the interpretations and significance of the results. Eventually, as multiple lines of evidence add weight to an idea, it becomes an integral part of the body of knowledge that exists in science and feeds back into the research process . Figure 5 provides a graphical overview of the materials we have developed to explain the real practice of science, and the key elements are described below.

The Scientific Community: Scientists (see our Scientists and the Scientific Community module) draw on their background, experiences, and even prejudices in deciding on the types of questions they pursue and the research methods that they employ, and they are supported in their efforts by the scientific institutions and the community in which they work (see our Scientific Institutions and Societies module). Human nature makes it impossible for any scientist to be completely objective, but an important aspect of scientific research is that scientists are open to any potential result. Science emphasizes the use of multiple lines of evidence as a check on the objectivity of both individual scientists and the community at large. Research is repeated, multiple methods are used to investigate the same phenomenon, and scientists report these methods and their interpretations when publishing their work. Assuring the objectivity of data and interpretation is built into the culture of science. These common practices unite a community of science made up of individuals and institutions that are dedicated to advancing science. Rowland, Molina, Lovelock, and Crutzen each were guided by their personal interests and supported by their respective institutions. For example, in addition to his work with CFCs, James Lovelock is credited with proposing the Gaia hypothesis that all living and non-living things on the planet interact with one another much like a large, single organism. This perspective influenced his interest in looking at the movement of large air masses across the globe, work that was supported by funding from the National Aeronautics and Space Administration (NASA).

Data: Science is a way of understanding the world around us that is founded on the principal of gathering and analyzing data (see our Data Analysis and Interpretation module). In contrast, before the popularization of science, philosophical explanations of natural phenomena based on reasoning rather than data were common, and these led to a host of unsupported ideas, many of which have proven incorrect. For example, in addition to his ideas on vision, the Greek philosopher Empedocles also reasoned that because most animals are warm to the touch, they must contain fire inside of them (see our States of Matter module). In contrast, the initial conclusion of the presence of a hole in the stratospheric ozone layer was based on years of data collected by scientists at the British Antarctic Survey. The amount of uncertainty and error (see our Uncertainty, Error, and Confidence module) associated with these data was critical to record as well – a small error in Dobson units would have made the hole seemingly disappear. Using statistical methods (see our Statistics in Science module) and data visualization techniques (see our Using Graphs and Visual Data in Science module) to analyze data, the scientists at the BAS drew on their own experience and knowledge to interpret those data, demonstrating that the “hole” was more than a seasonal, natural shift in ozone levels.

Ideas in science: Scientific research contributes to the body of scientific knowledge, held in record in the scientific literature (see our Utilizing the Scientific Literature module) so that future scientists can learn from past work. The literature does not simply hold a record of all of the data that scientists have collected: It also includes scientists’ interpretations of those data. To express their ideas, scientists propose hypotheses to explain observations. For example, after observing, collecting, and interpreting data, Lovelock hypothesized that CFCs could be used by meteorologists as benign tracers of the movement of large air masses. While Lovelock was correct in his prediction that CFCs could be used to trace air movement, later research showed that they are not benign. This hypothesis was just one piece of evidence that Molina and Rowland used to form their theory of ozone depletion. Scientific theories (see our Theories, Hypotheses, and Laws module) are ideas that have held up under scrutiny and are supported by multiple lines of evidence. The ozone depletion theory is based on results from all of the studies described above, not just Lovelock’s work. Unlike hypotheses, which can be tenuous in nature, theories rely on multiple lines of evidence and so are durable. Still, theories may change and be refined as new evidence and analyses come to light. For example, in 2007, a group of NASA scientists reported experimental results showing that chlorine peroxide, a compound formed when CFCs are transported to the stratosphere and which participates in the destruction of ozone, has a slower reaction rate in the presence of ultraviolet light than previously thought (Pope et al., 2007). The work by Pope and his colleagues does not dispute the theory of ozone destruction; rather, it does suggest that some modifications may be necessary in terms of the reaction rates used in atmospheric chemistry models.

Despite the fact that different scientists use different methods , they can easily share results and communicate with one another because of the common language that has developed to present and interpret data and construct ideas. These shared characteristics allow studies as disparate as atmospheric chemistry, plant biology, and paleontology to be grouped together under the heading of “science.” Although a practicing scientist in any one of those disciplines will require very specialized factual knowledge to conduct their research , the broad similarities in methodology allow that knowledge to be shared across many disciplines.

Scientists use multiple methods to investigate the natural world and these interconnect and overlap, often with unexpected results. This module gives an overview of scientific research methods, data processing, and the practice of science. It discusses myths that many people believe about the scientific method and provides an introduction to our Research Methods series.

Key Concepts

• The practice of science involves many possible pathways. The classic description of the scientific method as a linear or circular process does not adequately capture the dynamic yet rigorous nature of the practice.
• Scientists use multiple research methods to gather data and develop hypotheses. These methods include experimentation, description, comparison, and modeling.
• Scientific research methods are complementary; when multiple lines of evidence independently support one another, hypotheses are strengthened and confidence in scientific conclusions improves.

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• Published: 03 June 2024

Research on spatial carving method of glutenite reservoir based on opacity voxel imaging

• Hu Zhao 1 , 2 ,
• Zhong-wei Zhang 2 ,
• Hong-wei Yang 3 &
• Guo-hua Wei 3  

Scientific Reports volume  14 , Article number:  12667 ( 2024 ) Cite this article

Metrics details

• Energy science and technology
• Solid Earth sciences

The glutenite reservoir in an exploration area in eastern China is well-developed and holds significant exploration potential as an important oil and gas alternative layer. However, due to the influence of sedimentary characteristics, the glutenite reservoir exhibits strong lateral heterogeneity, significant vertical thickness variations, and low accuracy in reservoir space characterization, which affects the reasonable and effective deployment of development wells. Seismic data contains the three-dimensional spatial characteristics of geological bodies, but how to design a suitable transfer function to extract the nonlinear relationship between seismic data and reservoirs is crucial. At present, the transfer functions are concentrated in low-dimensional or high-dimensional fixed mathematical models, which cannot accurately describe the nonlinear relationship between seismic data and complex reservoirs, resulting in low spatial description accuracy of complex reservoirs. In this regard, this paper first utilizes a fusion method based on probability kernel to fuse seismic attributes such as wave impedance, effective bandwidth, and composite envelope difference. This provide a more intuitive reflection of the distribution characteristics of glutenite reservoirs. Moreover, a hybrid nonlinear transfer function is established to transform the fused attribute cube into an opaque attribute cube. Finally, the illumination model and ray casting method are used to perform voxel imaging of the glutenite reservoirs, brighten the detailed characteristics of reservoir space, and then form a set of methods for ' brightening reservoirs and darkening non-reservoirs ', which improves the spatial engraving accuracy of glutenite reservoirs.

Introduction

As the exploration and development targets gradually shift from structural oil and gas reservoirs to complex hidden oil and gas reservoirs, it is necessary to more accurately characterize the spatial distribution characteristics of reservoirs, establish a more refined reservoir model, and support the exploration and development of complex oil and gas reservoirs. Seismic data has always played an important role in reservoir characterization as it provides three-dimensional spatial information of geological bodies. Obviously, how to accurately mine the spatial characteristics of reservoir from seismic data is the key to solve the problem. Glutenite reservoir possess the characteristics such as strong concealment, rapid lithology change, complex reservoir connectivity and strong heterogeneity, making it difficult to accurately characterize its spatial characteristics. Therefore, in order to better explore and develop glutenite reservoirs, it is essential to investigate the technology that can accurately characterize their spatial characteristics. Direct volume rendering, as one of the most effective volume data visualization methods for visualizing volume data, involves converting each data point in the volume data into voxels with specific color values and opacity through the transfer function. Subsequently, the changes in light passing through each voxel are analyzed to depict the internal characteristics of the volume data and selectively display or shield some data volume characteristics. Therefore, it is widely used in medical imaging, seismic exploration and other fields.

In the field of seismic exploration, Gerald (1999) used the method of direct volume rendering to convert each data point of 3D seismic data into voxels with specific color and luminosity, thereby achieving the spatial characterization of 3D seismic data 1 . Ropinski et al. (2005) utilized volume rendering technology for three-dimensional visualization of seismic data and proposed the concept of lens. Users can observe the internal characteristics of the data volume of interest through the lens 2 . Zhang (2007) proposed a direct volume rendering method for seismic data based on Shear-Warp in a virtual reality environment, which enabled the stereoscopic display of data volume by combining perspective projection 3 . Li (2008) realized the three-dimensional visualization of seismic data by using the parallel projection-based footprint table method 4 . Qin (2013) proposed a multi-dimensional transfer function setting method based on parallel coordinates. Its application can reduce the dimension of high-dimensional space to the parallel coordinates of two-dimensional space to solve the complex and difficult problem of multi-dimensional transfer function setting 5 . Duan et al. (2013) applied GPU-accelerated ray casting volume rendering method to seismic interpretation, and realized the clear display of horizon spatial distribution characteristics through interactive design transfer function 6 . Chen (2018) analyzed the process of realizing 3D visualization of seismic data using the direct volume rendering method, and realized 3D visualization of seismic data by using Coin3D through ray projection algorithm and histogram-based transfer function 7 . Obviously, the predecessors have used the volume rendering method to realize the spatial characterization of seismic data, but the above authors have used one-dimensional transfer function based on scalar value or high-dimensional transfer function based on fixed mathematical model in volume rendering. Although the overall three-dimensional characterization of seismic data volume has been realized, the nonlinear relationship between seismic data and reservoir has not been accurately characterized, resulting in low accuracy of reservoir spatial description. There is a strong nonlinear relationship between seismic data and reservoir. The design of transfer function holds great significance as they are responsible for classifying voxels and assigning optical attributes (color and opacity) in volume rendering. By adjusting the transfer function, the reservoir and non-reservoir in seismic data volume can be accurately distinguished, and the reservoir can be highlighted, so as to achieve the purpose of ' brightening the reservoir and shielding the non-reservoir '. At present, many transfer function design methods have been proposed. To solve the problem of insufficient classification ability of one-dimensional transfer function, researchers have proposed to increase the gradient 8 , curvature 9 , space 10 , texture 11 , size 12 and other information to design multi-dimensional transfer function. However, multi-dimensional transfer function has problems such as design complexity and difficulty in understanding. In this regard, researchers have proposed the use of parallel coordinates to reduce multidimensional space into two-dimensional space, aiding in the design of multidimensional functions 13 or the use of hierarchical clustering 14 , density clustering 15 and probability model clustering 1 to achieve adaptive generation of multidimensional transfer functions. However, existing transfer function design methods for this purpose have certain limitations. Some methods lack sufficient classification ability, while others are complex, difficult to comprehend, or have inconvenient user interfaces. Moreover, these methods are primarily designed for medical applications and may not be fully applicable to the field of seismic exploration due to the distinct characteristics of seismic exploration data.

In order to better separate the reservoir and non-reservoir in seismic data volume and accurately describe the spatial distribution characteristics of the reservoir, this paper intends to explore the attribute fusion method based on the probability kernel. The objective is to excavate common characteristics between seismic attributes and highlight the characteristics of the reservoir. Subsequently, a hybrid nonlinear transfer function is established to transform the fused attribute cube into a light-blocking attribute cube. The ray projection method of the illumination model is used for reservoir voxel imaging to shield non-reservoir, and finally complete the characterization of the three-dimensional spatial characteristics of the reservoir.

The exploration practice shows that the glutenite reservoir has the characteristics of large lateral thickness variations and strong heterogeneity in physical properties, which affects the subsequent reservoir evaluation and the design of development well location. In this paper, based on the seismic attribute, the probability kernel attribute fusion technology is established to excavate the common characteristics between seismic attributes and further highlight the characteristics of glutenite reservoir.

An attribute fusion method based on probability kernel

There is a nonlinear relationship between seismic attributes and glutenite reservoirs. It is necessary to carry out volume fusion processing to explore the characteristics of reservoirs between seismic attributes.

Firstly, different seismic body attribute data are selected, and the original data of seismic attributes are extracted by the following equation to prepare for fusion :

Which \(x_{i}\) is the sample point in an individual attribute.

Due to the nonlinear relationship between seismic body attributes and glutenite reservoirs, it is not possible to directly indicate reservoir characteristics. Therefore, it is necessary to perform nonlinear mapping on the body attributes involved in the fusion. Firstly, different body attribute data are normalized. On this basis, the kernel function is selected for mapping. The Gaussian kernel function designed in this paper is as follows.

The characteristic of the kernel function is that it has good anti-interference ability to the noise in the data, and can establish a nonlinear relationship between seismic attributes and reservoirs. The above kernel function is used for mapping calculation as follows, \(\Phi :x \to \Phi (x) \in R^{f}\) .

where : \(\Phi (X)\) is the feature space of \(X\) .

Then calculate the eigenvalue \(\Lambda\) and eigenvector \(V\) of Eq. ( 3 ), and arrange them from large to small according to the value, and take the first q eigenvalues with larger eigenvalue contribution rate and the corresponding eigenvectors. The larger the eigenvalue, the stronger the common characteristic.

where \(V\) is the set of eigenvectors \(v_{i}\) corresponding to the eigenvalue \(\lambda_{{\text{i}}}\) , and \(\Lambda\) is the diagonal matrix composed of eigenvalues.

The feature vector set of high dimensional space is established by using Eqs. ( 4 ) and ( 5 ).

Here, \(U\) is a set of high-dimensional space feature vectors of \(u_{i}\) , \(\Phi\) is the feature space mapped by high-dimensional space, and let \(J = N^{{ - \frac{1}{2}}} ({\rm I}_{N} - e{\rm I}^{T} )\) , \(\Phi J\) be the centralization of high-dimensional feature space data.

In order to extract the nonlinear characteristics between the seismic attribute cube more effectively, the Bayesian probability model is used to discard the redundant data. Firstly, the maximum likelihood function estimation parameters are calculated according to the Bayesian probability model using Eq. ( 7 ).

And calculate the sample mean:

Using iterative algorithm to calculate \(W\) , \(\rho\) , make it converge :

where \(W\) is the load matrix, \(Q_{t}\) and \(\rho_{t}\) and \(Q_{t + 1}\) and \(\rho_{t + 1}\) are the empirical load matrix and variance before iteration and the empirical load matrix and variance after iteration, respectively.

Finally, the volume fusion calculation is carried out by the following equation.

where \(R\) is an arbitrary orthogonal matrix.

The key step is Eq. ( 3 ). The kernel function is used to map the nonlinear relationship. As shown in Fig.  1 a, there are three kinds of seismic attribute data in two-dimensional space, which have a common center, but cannot be effectively separated. The data can be mapped to high-dimensional space by Eq. ( 3 ), and the data can be mapped and separated in high-dimensional space and the common center can be identified (Fig.  1 b). On this basis, the kernel matrix after mapping is solved, and the feature vector (Eq.  6 ) of high-dimensional space is established by using Eqs. ( 4 ) and ( 5 ), and the solution is carried out. Finally, the attribute data fusion calculation (mining the common features between attributes) is completed.

Data nonlinear mapping diagram. ( a ) Seismic attribute data diagram before mapping. ( b ) Seismic attribute data in high-dimensional space after mapping.

A volume space characterization method based on opacity voxel imaging

The main idea of seismic opacity voxel imaging is to use the transfer function to map the seismic attributes into the opacity, so as to establish the opacity model and the illumination model, simulate the process of reflection and projection generated by the light penetrating the geological body, and finally calculate the light intensity of each data point in the space. Then the light intensity of each data projected onto the same pixel on the image plane is superimposed to form a visual image. The core problem is the design of the opacity model and the illumination model, which is directly related to the effect of volume rendering.

Opacity model design: the opacity model is to convert seismic data into optical impedance value, because seismic data has strong nonlinear characteristics, so the common mathematical functions cannot accurately express the nonlinear relationship between it and the reservoir, in this paper based on the Poisson transfer function (Eq.  12 ) to construct a hybrid nonlinear opacity model (Fig.  2 ), which has the characteristics of more concentrated numerical distribution and fast attenuation, which can easily excavate reservoirs from the formation.

Multi-point Poisson resistance model.

The smaller the value of \(\lambda\) is, the more biased the distribution is. With the increase of \(\lambda\) , the distribution tends to be symmetrical.

Three interpolation volume space resampling calculation ; because the three-dimensional data volume is discretely distributed, the light is transmitted directly, and the screen pixel value can only be calculated (voxel) by relying on the data at the grid points. Therefore, the light may be projected from the gap to the visual plane, resulting in a discontinuous imaging effect of the projected object (Fig.  3 ),. This requires the resampling calculation of the 3D data field according to the spatial direction of the light penetrating the 3D data volume. This time, the cubic interpolation volume space resampling algorithm is used to solve this problem. The theoretical equation is as follows.

Cubic interpolation volume space resampling schematic diagram.

Among them, point \(p\) is any point in space that needs to be resampled, \(p_{1}\) and \(p_{2}\) are the points where point \(p\) is projected into the data grid, and \(y_{p}\) is the \(y\) coordinate of point \(p\) .

Lighting model design; In the lighting model, it is assumed that the continuously distributed three-dimensional data field is full of small particles, which can emit light, and the light intensity of each particle reaching the viewpoint is the attenuated light intensity, and the contribution of each particle to the light intensity of the pixel can be accumulated along the line of sight to obtain the final light intensity of the pixel. Then:

where \(s\) is the light propagation length parameter, \(\tau ({\text{s}}) = \rho (s) \cdot A\) is the ray attenuation coefficient, which represents the absorption rate of light by the medium when the ray propagation distance is \(s\) . \(I(s)\) is the light intensity of the incident light.

Move \(\tau (s)I(s)\) from the above differential equation to the left of the equation:

Multiply both sides of the equation by \({\text{exp}}(\int_{0}^{s} {\tau (t)dt)}\) :

Integrating both sides of the above equation from the edge of the 3D volume data (S = 0) to the observer point (S = D) yields:

where \(I_{0}\) is the initial light intensity of the incident light.

The above equation can be simplified to:

The approximate numerical solution of the above equation is as follows:

Using the above equation, the light intensity value of the incident light penetrates the object to the observer point can be calculated, and the optical resistance value a corresponding to the propagation distance is usually defined:

According to the above theory, if the luminous intensity \(C\) of three-dimensional particles is constant, or the color value \(C\) assigned to similar substances is constant, then after a propagation distance \(D\) , the light intensity \(I(D)\) of the light reaching the viewpoint is:

In the above equation, \(T(D)\) is the transparency of this medium of length \(D\) , and \(I_{0}\) is the intensity of the incoming background light. This equation expresses the combined light intensity of the background light \(I_{0}\) and the light source assigned the color value \(C\) under the action of transparency \(T(D)\) . The \(\alpha\) of 0 means that the medium is completely transparent, so it is invisible, but objects behind it are visible. It is this idea that excavates the characteristics of sandstone and conglomerate reservoirs in sandstone mudstone, and then completes the volume space carving.

Application in field data

Regional geological overview.

The study area is located in the Yan16 ancient gully in the northern steep slope zone of Dongying Sag in the Jiyang Depression of the Bohai Bay Basin. The main exploration target in this area is the shallow glutenite reservoir, and the oil-bearing area is controlled by the morphology and continuity of the sandstone reservoir. The glutenite primarily represents deposition of a nearshore subaqueous fan system with sediment sources originating from the northern Chenjiazhuang uplift. The lithology is mainly composed of gravel-bearing fine sandstone, interbedded with dark gray and gray mudstone. The reservoir properties are relatively good, but there is a significant variation in spatial continuity, which affects the migration and accumulation of oil and gas. From the Fig.  4 , it can be seen that the glutenite reservoir exhibits medium—strong amplitude, bright spots, but with poor continuity in seismic profile. Additionally, the glutenite overlays the basement of the Sinian Formation (This is shown in the yellow box in the figure).

Typical seismic profiles in different directions. ( a ) Seismic profile across wells in east–west direction [the direction of the red arrow in the figure is the direction of the red line arrow parallel to the L number axis of the navigation diagram in ( c )]. ( b ) Seismic profile across well Y16X18 in north–south direction [the direction of the red arrow in the figure is the direction of the red line arrow parallel to the X number axis of the navigation diagram in ( c )]. ( c ) Navigation map(X number is CDP number; L number is inline number).

Application

There is strong lateral heterogeneity in the reservoirs in the study area, for example, well Y16-X19B is effective 11 days after water injection during the development process, while well Y16-X23 at the same distance is indeed ineffective. This clearly indicates a reservoir connectivity issue, highlighting the need for accurately describing the spatial distribution of reservoir. To solve this problem, we first selected sensitive seismic attributes, including effective bandwidth, composite envelope difference and impedance. These attributes can reflect the scale and degree of reservoir development, with higher value indicating more developed reservoirs (Fig.  5 ). Next, the probabilistic kernel method is used to mine the common features between these attributes. Figure  6 shows the only seismic attribute cube obtained from the fusion process, which reflects the three-dimensional characteristics of all geological bodies in the subsurface. This fused attribute cube serves as the basis and data source of seismic exploration. However, due to the influence of non-reservoirs, the spatial distribution of the glutenite reservoir cannot be fully characterized. Therefore, according to the proposed method in this paper, appropriate transfer function were selected to establish an opacity model, enabling reservoir extraction and non-reservoir masking.

Seismic attribute cube (effective bandwidth on the left, composite envelope difference on the right).

The fused attribute cube.

The traditional method of shielding non-reservoirs is to set a threshold. However, the division boundary between the glutenite reservoir and the non-reservoir is unclear, and the reservoir itself may have multiple ranges of attribute values due to sedimentary processes. Therefore, an opacity model with mixed multi-segment nonlinear transfer function was established according to the characteristics of the glutenite reservoir data (Fig.  8 ), and corresponding voxel imaging data was obtained(Fig.  7 , the more developed the reservoir, the higher the opacity value, with a non-reservoir opacity value of 0). However, in Fig.  7 , the spatial features of the glutenite reservoir are still not observable as numerous geological bodies in the non-target layers obstruct the light penetration. To address this, further darkening of the non-reservoir regions was performed. This involved selecting the top and bottom of the target layer for surface cutting, resulting in all geological bodies outside the target layer becoming darker (Fig.  8 ). Finally, the reservoir is illuminated and non-reservoir layers are darkened through the application of series of technologies (Fig.  9 ).

Attribute cube after opacity voxel imaging.

Hybrid multi-segment nonlinear opacity model (the abscissa represents the fused attribute value, and the ordinate represents the opacity value).

Reservoir characteristics after opacity voxel imaging + target layer surface cropping + volume space rendering. ( a ) Front view, ( b ) oblique view, ( c ) front view (traditional methods), ( d ) oblique view (traditional methods), ( e ) the Gaussian function resistance model (traditional methods).

In order to show the detailed characteristics of the reservoir, it is necessary to further brighten the reservoir and perform volume rendering calculations to enhance the degree of brightness at each point within the reservoir. Figure  9 a, b shows that the application of opacity voxel imaging technology greatly improve the accuracy of reservoir identification. The spatial distribution characteristics of reservoirs are clearly visible, and the lateral continuity is also intuitively reflected. Figure  9 c, d display the glutenite reservoir identified using traditional methods (the Gaussian function resistance model, Fig.  9 e). Compared with the identification results in Fig.  9 a, the traditional method did not effectively excavate the spatial distribution characteristics of the glutenite reservoir on the south side of the study area. In Fig.  9 c, the glutenite reservoir on the south side of the study area presents sheet-like characteristics and lacks changes in horizontal heterogeneity. This does not match the actual geological characteristics. The effectiveness of reservoir identification using hybrid multi-segment nonlinear models surpasses that of traditional methods. On this basis, the glutenite reservoir for opacity voxel imaging can be quantified and output, and the output data only contains the data of reservoir points in space, with non-reservoir points assigned 0 value. This data can be applied to reservoir modeling in the later stage. Obviously, the data obtained from the hybrid multi-segment nonlinear opacity model almost only contains reservoir information, and non-reservoir data has been shielded, so the reservoir model established by it will be more reliable.

In order to further validate the accuracy of the proposed method, the identified glutenite reservoir was superimposed with seismic data. Figure  9 shows the seismic response characteristics of the glutenite reservoir (red in figure), including medium—strong amplitude, bright spots, discontinuity, and poor continuity. These characteristics are consistent with previous study. In Fig.  10 a, it can be seen that the glutenite reservoir between the two wells exhibits good connectivity. This observation aligns with the dynamic production data. Specifically, when water injection occurs in the well Y16X25, there is a significant increase in the water content of the well Y16X20 (Y16X20 has not been injected water). This also supports the reservoir identification results and the actual underground situation. In Fig.  10 b, the seismic events between well Y16X27, well Y16X23 and well Y16X26 exhibit good continuity and stable waveform. This suggests that the glutenite reservoir is poorly developed and the mudstone is more developed, so the reservoir connectivity is general. Moreover, the dynamic production data shows that there is no significant response observed between wells Y16X23 and Y16X26 during water injection in well Y16X27, which further supports the reliability of the method of reservoir identification in this paper.

Seismic data, co-rendered with reservoir identified by the proposed method, showing the continuity of reservoir. ( a ) Seismic profile across Well Y16X25. ( b ) Seismic profile across Well Y16X27.

Conclusions

Usually we extract spatial information of reservoir from seismic data and adopt both linear and nonlinear method to establish a mapping relationship between seismic data and reservoirs. However, we rarely investigate whether this function can effectively express the nonlinear relationship between them. Practice suggested that a single low-dimensional or high-dimensional function can not express such a relationship. Therefore, it is necessary to establish a hybrid nonlinear function to accurately express such a nonlinear mapping relationship, enabling an accurate depiction of the volumetric spatial nonlinear relationship between reservoirs and seismic attributes.

Opacity voxel imaging technology can intuitively construct multi-segment, multi-type hybrid nonlinear functions, which can map the most favorable seismic information into optical information. This technology demonstrates strong intuitiveness, and the quantification results pertaining to reservoirs align closely with the geological conditions. It significantly enhances the characterization of the spatial distribution characteristics of reservoirs, making the data becomes more reliable for reservoir modeling.

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

We are grateful to the reviewers for a thorough reading and constructive comments on this paper. This research is supported by innovation consortium project of China Petroleum and Southwest Petroleum University (No. 2020CX010201), Natural Science Foundation Project of Sichuan (No. 2024NSFSC0081).

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School of Geoscisence and Technology, Southwest Petroleum University, Chengdu, 610500, China

Hu Zhao & Zhong-wei Zhang

Geophysical Exploration Institute, Shengli Oilfield Company, SINOPEC, Dongying, 257000, China

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ZHAO Hu and ZHANG Zhong-wei wrote the main manuscript text. YANG Hong-wei and WEI Guo-hua. prepared figures 2-4. All authors reviewed the manuscript.

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Zhao, H., Zhang, Zw., Yang, Hw. et al. Research on spatial carving method of glutenite reservoir based on opacity voxel imaging. Sci Rep 14 , 12667 (2024). https://doi.org/10.1038/s41598-024-63643-2

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Secrets of radioactive 'promethium' — a rare earth element with mysterious applications — uncovered after 80-year search

Scientists have revealed key properties of radioactive promethium, a rare earth element with poorly understood applications, using a groundbreaking new method.

For the first time, scientists have revealed crucial properties of the mysterious, radioactive substance promethium — nearly eight decades after the elusive rare earth element was discovered.

Promethium is one of the 15 lanthanide elements at the bottom of the periodic table. Also known as the rare earths, these metals exhibit a number of useful properties, including strong magnetism and unusual optical characteristics, making them particularly important in modern electronic devices. 

"They are used in lasers ; they are part of the screens of your smartphone. They are also used in very strong magnets in wind turbines and electric vehicles," Ilja Popovs , a research and development staff member at Oak Ridge National Laboratory (ORNL) and co-author of a new study published in the journal Nature , told Live Science. 

'Scarce and difficult to study'

Promethium itself, which was discovered by ORNL scientists in 1945 , has a few minor applications in atomic batteries and cancer diagnostics. But scientists have a very limited understanding of the element's chemistry, precluding more widespread uses. 

Studying the radioactive element has posed a decades-long challenge, partly due to the difficulty of securing a suitable sample, team member Alexander Ivanov , also a research and development scientist at ORNL, told Live Science.

"Promethium doesn't have a stable isotope — they're all radioactive, meaning that they are decaying [into other elements] with time," Ivanov said. "You get this element through a fission process, so it's scarce and difficult to study."

ORNL is the U.S.' only producer of promethium-147, an isotope of the element with a radioactive half-life of 2.6 years. Using a method developed last year , the researchers separated this isotope from nuclear reactor waste streams, creating the purest possible sample for study.

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Then, the team combined this sample with a ligand — a molecule specially designed to trap metal atoms — to form a stable complex in water. The coordinating molecule, known as PyDGA, formed nine promethium-oxygen bonds, giving researchers the first-ever opportunity to analyze the bonding properties of a promethium complex.

However, the analysis itself was no trivial matter. 

"Because promethium is radioactive, once it's decaying, it's getting transmuted into the adjacent element, which is samarium ," Ivanov said. "So you will have a tiny amount of contamination in the form of samarium." 

'The last puzle piece'

The team therefore used an extremely specialized, element-specific technique called synchrotron-based X-ray absorption spectroscopy. High-energy photons generated by a particle accelerator bombarded the promethium complex to build a picture of the positions of atoms and the lengths of bonds. Subtle differences in the metal-oxygen bond lengths then allowed the team to focus on the key promethium-oxygen bond, discounting any contaminating samarium.

Crucially, this information enabled a comparison of promethium's properties with other rare earth complexes for the first time. 

"Promethium was the last puzzle piece among those elements," Popovs said. The ligand provided a way to have a stable complex for all of the lanthanides — the same element ratios and the same kind of geometry. That allowed the team to "study the fundamental physical chemical properties of these complexes across the whole series," Popovs explained.

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Lanthanides are naturally found as mixtures of elements, so understanding periodic trends such as bond lengths and complex-forming behaviors helps scientists develop new and more efficient methods to separate these valuable metals. 

Now, the ORNL team is studying promethium in water to build a clearer picture of the coordination environment and chemical behavior of this unusual element. 

"Hopefully, the fundamental insights that we're providing will inform other scientists how to design better separation technologies and can perhaps spur more interest in studying it for other applications," Popovs said.

Victoria Atkinson is a freelance science journalist, specializing in chemistry and its interface with the natural and human-made worlds. Currently based in York (UK), she formerly worked as a science content developer at the University of Oxford, and later as a member of the Chemistry World editorial team. Since becoming a freelancer, Victoria has expanded her focus to explore topics from across the sciences and has also worked with Chemistry Review, Neon Squid Publishing and the Open University, amongst others. She has a DPhil in organic chemistry from the University of Oxford.

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