ppt on hypothesis in research methodology

Please log in to save materials. Log in

• Resource Library
• Research Methods
• VIVA Grant Recipients
• Vgr-social-work-research

Education Standards

Radford university.

Learning Domain: Social Work

Standard: Basic Research Methodology

Lesson 10: Sampling in Qualitative Research

Lesson 11: qualitative measurement & rigor, lesson 12: qualitative design & data gathering, lesson 1: introduction to research, lesson 2: getting started with your research project, lesson 3: critical information literacy, lesson 4: paradigm, theory, and causality, lesson 5: research questions, lesson 6: ethics, lesson 7: measurement in quantitative research, lesson 8: sampling in quantitative research, lesson 9: quantitative research designs, powerpoint slides: sowk 621.01: research i: basic research methodology.

The twelve lessons for SOWK 621.01: Research I: Basic Research Methodology as previously taught by Dr. Matthew DeCarlo at Radford University. Dr. DeCarlo and his team developed a complete package of materials that includes a textbook, ancillary materials, and a student workbook as part of a VIVA Open Course Grant.

The PowerPoint slides associated with the twelve lessons of the course, SOWK 621.01: Research I: Basic Research Methodology, as previously taught by Dr. Matthew DeCarlo at Radford University. 

Chapter 7 Hypothesis Testing

Aug 14, 2012

860 likes | 1.94k Views

7-1 Basics of Hypothesis Testing 7-2 Testing a Claim about a Mean: Large Samples 7-3 Testing a Claim about a Mean: Small Samples 7-4 Testing a Claim about a Proportion 7- 5 Testing a Claim about a Standard     Deviation (will cover with chap 8). Chapter 7 Hypothesis Testing. 7-1.

Share Presentation

• standard deviation s
• exam questions
• significance level
• large samples

Presentation Transcript

7-1 Basics of Hypothesis Testing 7-2 Testing a Claim about a Mean: Large Samples 7-3 Testing a Claim about a Mean: Small Samples 7-4 Testing a Claim about a Proportion 7- 5 Testing a Claim about a Standard     Deviation (will cover with chap 8) Chapter 7Hypothesis Testing

7-1 Basics of Hypothesis Testing

Hypothesis in statistics, is a statement regarding a characteristic of one or more populations Definition

Statement is made about the population Evidence in collected to test the statement Data is analyzed to assess the plausibility of the statement Steps in Hypothesis Testing

Components of aFormal Hypothesis Test

Form Hypothesis Calculate Test Statistic Choose Significance Level Find Critical Value(s) Conclusion Components of a Hypothesis Test

A hypothesis set up to be nullified or refuted in order to support an alternate hypothesis. When used, the null hypothesis is presumed true until statistical evidence in the form of a hypothesis test indicates otherwise. Null Hypothesis: H0

Statement about value of population parameter like m, p or s Must contain condition of equality =, , or Test the Null Hypothesis directly RejectH0 or fail to rejectH0 Null Hypothesis: H0

Must be true if H0 is false , <, > ‘opposite’ of Null sometimes used instead of Alternative Hypothesis: H1 H1 Ha

If you are conducting a study and want to use a hypothesis test to support your claim, the claim must be worded so that it becomes the alternative hypothesis. The null hypothesis must contain the condition of equality Note about Forming Your Own Claims (Hypotheses)

Set up the null and alternative hypothesis The packaging on a lightbulb states that the bulb will last 500 hours. A consumer advocate would like to know if the mean lifetime of a bulb is different than 500 hours. A drug to lower blood pressure advertises that it drops blood pressure by 20%. A doctor that prescribes this medication believes that it is less. Set up the null and alternative hypothesis. (see hw # 1) Examples

a value computed from the sample data that is used in making the decision about the rejection of the null hypothesis Testing claims about the population proportion Test Statistic x - µ σ Z*= n

Critical Region - Set of all values of the test statistic that would cause a rejection of the null hypothesis Critical Value - Value or values that separate the critical region from the values of the test statistics that do not lead to a rejection of the null hypothesis

One Tailed Test Critical Region and Critical Value Critical Region Critical Value ( z score )

Two Tailed Test Critical Region and Critical Value Critical Regions Critical Value ( z score ) Critical Value ( z score )

Denoted by  The probability that the test statistic will fall in the critical region when the null hypothesis is actually true. Common choices are 0.05, 0.01, and 0.10 Significance Level

Two-tailed,Right-tailed,Left-tailed Tests The tails in a distribution are the extreme regions bounded by critical values.

H0: µ = 100 H1: µ  100 Two-tailed Test  is divided equally between the two tails of the critical region Means less than or greater than Reject H0 Fail to reject H0 Reject H0 100 Values that differ significantly from 100

H0: µ  100 H1: µ > 100 Fail to reject H0 Reject H0 Right-tailed Test Points Right Values that differ significantly from 100 100

H0: µ  100 H1: µ < 100 Left-tailed Test Points Left Reject H0 Fail to reject H0 Values that differ significantly from 100 100

Traditional Method Reject H0if the test statistic falls in the critical region Fail to reject H0if the test statistic does not fall in the critical region P-Value Method Reject H0if the P-value is less than or equal  Fail to reject H0if the P-value is greater than the  Conclusions in Hypothesis Testing

Finds the probability (P-value) of getting a result and rejects the null hypothesis if that probability is very low Uses test statistic to find the probability. Method used by most computer programs and calculators. Will prefer that you use the traditional method on HW and Tests P-Value Methodof Testing Hypotheses

Two tailed test p(z>a) + p(z<-a) One tailed test (right) p(z>a) One tailed test (left) p(z<-a) Finding P-values Where “a” is the value of the calculated test statistic Used for HW # 3 – 5 – see example on next two slides

Determine P-value Sample data: x = 105 or z* = 2.66 Reject H0: µ = 100 Fail to Reject H0: µ = 100 * µ = 73.4 or z = 0 z = 1.96 z* = 2.66 Just find p(z > 2.66)

Determine P-value Sample data: x = 105 or z* = 2.66 Reject H0: µ = 100 Reject H0: µ = 100 Fail to Reject H0: µ = 100 * z = - 1.96 µ = 73.4 or z = 0 z = 1.96 z* = 2.66 Just find p(z > 2.66) + p(z < -2.66)

Always test the null hypothesis Choose one of two possible conclusions 1. Reject the H0 2. Fail to reject the H0 Conclusions in Hypothesis Testing

Never “accept the null hypothesis, we will fail to reject it. Will discuss this in more detail in a moment We are not proving the null hypothesis Sample evidence is not strong enough to warrant rejection (such as not enough evidence to convict a suspect – guilty vs. not guilty) Accept versus Fail to Reject

Accept versus Fail to Reject

Need to formulate correct wording of finalconclusion Conclusions in Hypothesis Testing

Wording of final conclusion 1. Reject the H0 Conclusion: There is sufficient evidence to conclude………………………(what ever H1 says) 2. Fail to reject the H0 Conclusion: There is not sufficient evidence to conclude ……………………(what ever H1 says) Conclusions in Hypothesis Testing

State a conclusion The proportion of college graduates how smoke is less than 27%. Reject Ho: The mean weights of men at FLC is different from 180 lbs. Fail to Reject Ho: Example Used for #6 on HW

The mistake of rejecting the null hypothesis when it is true. (alpha) is used to represent the probability of a type I error Example: Rejecting a claim that the mean body temperature is 98.6 degrees when the mean really does equal 98.6 (test question) Type I Error

the mistake of failing to reject the null hypothesis when it is false. ß (beta) is used to represent the probability of a type II error Example: Failing to reject the claim that the mean body temperature is 98.6 degrees when the mean is really different from 98.6 (test question) Type II Error

Type I and Type II Errors True State of Nature H0 True H0 False Reject H0 Correct decision Type I error  Decision Fail to Reject H0 Type II error  Correct decision In this class we will focus on controlling a Type I error. However, you will have one question on the exam asking you to differentiate between the two.

a = p(rejecting a true null hypothesis) b = p(failing to reject a false null hypothesis) n, a and b are all related Type I and Type II Errors

Identify the type I and type II error. The mean IQ of statistics teachers is greater than 120. Type I: We reject the mean IQ of statistics teachers is 120 when it really is 120. Type II: We fail to reject the mean IQ of statistics teachers is 120 when it really isn’t 120. Example

For any fixed sample size n, as  decreases,  increases and conversely. To decrease both  and , increase the sample size. Controlling Type I and Type II Errors

Power of a Hypothesis Test is the probability (1 - ) of rejecting a false null hypothesis. Note: No exam questions on this. Usually covered in a more advanced class in statistics. Definition

7-2 Testing a claim about the mean (large samples)

Goal Identify a sample result that is significantly different from the claimed value By Comparing the test statistic to the critical value Traditional (or Classical) Method of Testing Hypotheses

Determine H0 and H1. (and if necessary) Determine the correct test statistic and calculate. Determine the critical values, the critical region and sketch a graph. Determine Reject H0 or Fail to reject H0 State your conclusion in simple non technical terms. Traditional (or Classical) Method of Testing Hypotheses (MAKE SURE THIS IS IN YOUR NOTES)

Test Statistic for Testing a Claim about a Proportion Can Use Traditional method Or P-value method

1) Traditional method 2) P-value method 3) Confidence intervals Three Methods Discussed

for testing claims about population means 1) The sample is a random sample. 2) The sample is large (n > 30). a) Central limit theorem applies b) Can use normal distribution 3) If  is unknown, we can use sample standard deviation s as estimate for . Assumptions

Test Statistic for Claims about µ when n > 30 x - µx Z*=  n

Reject the null hypothesis if the test statistic is in the critical region Fail to reject the null hypothesis if the test statistic is not in the critical region Decision Criterion

Claim:  = 69.5 years H0 :  = 69.5 H1 :  69.5 Example:A newspaper article noted that the mean life span for 35 male symphony conductors was 73.4 years, in contrast to the mean of 69.5 years for males in the general population. Test the claim that there is a difference. Assume a standard deviation of 8.7 years. Choose your own significance level. Step 1: Set up Claim, H0, H1 Select if necessary level:  = 0.05

Step 2: Identify the test statistic and calculate x - µ 73.4 – 69.5 z*=== 2.65  8.7 n 35

Step 3: Determine critical region(s) and critical value(s) & Sketch = 0.05 /2= 0.025 (two tailed test) 0.4750 0.4750 0.025 0.025 z = - 1.96 1.96 Critical Values - Calculator

• More by User

Chapter 8 Hypothesis Testing

Chapter 8 Hypothesis Testing. 8.2 Basics of Hypothesis Testing 8.3 Testing about a Proportion p 8.4 Testing about a Mean µ ( σ known ) 8.5 Testing about a Mean µ ( σ unknown ) 8.6 Testing about a Standard Deviation σ. Section 8.2 Basics of Hypothesis Testing. Objective

1.18k views • 65 slides

Chapter 9 Hypothesis Testing

Chapter 9 Hypothesis Testing. 9.1 The Language of Hypothesis Testing. Example: Illustrating Hypothesis Testing.

934 views • 32 slides

Chapter 9 Hypothesis Testing. 9.1 The Language of Hypothesis Testing. Steps in Hypothesis Testing 1. A claim is made. Steps in Hypothesis Testing 1. A claim is made. 2. Evidence (sample data) is collected in order to test the claim. Steps in Hypothesis Testing 1. A claim is made.

673 views • 30 slides

Chapter 9 Hypothesis Testing. Developing Null and Alternative Hypotheses Type I and Type II Errors One-Tailed Tests About a Population Mean: Large-Sample Case Two-Tailed Tests About a Population Mean: Large-Sample Case Tests About a Population Mean: Small-Sample Case

658 views • 43 slides

Chapter 9 Hypothesis Testing. Testing Hypothesis about µ, when the s.t of population is known. THREE WAYS TO STRUCTURE THE HYPOTHESIS TEST:.

526 views • 36 slides

Chapter 10: Hypothesis Testing

Chapter 10: Hypothesis Testing. Outline (Topics from 10.2 and 10.4). Hypothesis Testing Definitions The p value Examples and summary of steps Significance levels. Z-test for means and proportions. Tests of significance. How do we determine how good our estimate of s parameter is?

1.22k views • 36 slides

Chapter 8 Hypothesis Testing. Definitions 1 Sample Mean Z-test 1 Sample Mean T-test 1 Proportion Z-test 2 Independent Samples T-test 2 Related Samples Paired Data Type of Errors. Definition. Hypotheses Test Statistic P-value Decision and Conclusion. Definition.

677 views • 34 slides

Chapter 9 Hypothesis Testing. Developing Null and Alternative Hypotheses. Type I and Type II Errors. Population Mean: s Known. Population Mean: s Unknown. Developing Null and Alternative Hypotheses. Hypothesis testing can be used to determine whether

486 views • 21 slides

Chapter 8 Hypothesis Testing. 8-1 Review and Preview 8-2 Basics of Hypothesis Testing 8-3 Testing a Claim about a Proportion 8-4 Testing a Claim About a Mean: Known 8-5 Testing a Claim About a Mean: Not Known 8-6 Testing a Claim About a Standard Deviation or Variance.

2.11k views • 103 slides

Chapter 6 Hypothesis Testing

Chapter 6 Hypothesis Testing. Standard Deviation. Regression. Dependent variable. Independent variable (x). Regression is the attempt to explain the variation in a dependent variable using the variation in independent variables. Regression is thus an explanation of causation.

826 views • 65 slides

Chapter 7: Hypothesis Testing

Chapter 7: Hypothesis Testing. A hypothesis is a conjecture about a population. Typically, these hypotheses will be stated in terms of a parameter. A test of hypothesis is a statistical procedure used to make a decision about the conjectured value of a parameter.

1.03k views • 31 slides

Chapter 10 – Hypothesis Testing

Chapter 10 – Hypothesis Testing. What is a hypothesis? A statement about a population that may or may not be true. What is hypothesis testing? A statistical test to prove or disprove a hypothesis. At the end of the test, either the hypothesis is rejected or not rejected.

653 views • 31 slides

Chapter 9 Hypothesis Testing. Introduction to Statistical Tests Testing the Mean µ Testing a Proportion p Tests Involving Paired Differences Testing µ1-µ2 and p1-p2. 9.1 Introduction to Statistical Tests. We can draw inference on a population parameter in two ways: Estimation (Chapter 8)

984 views • 78 slides

Chapter 8 Hypothesis Testing. 8-1 Review and Preview 8-2 Basics of Hypothesis Testing 8-3 Testing a Claim about a Proportion 8-4 Testing a Claim About a Mean: σ Known 8-5 Testing a Claim About a Mean: σ Not Known 8-6 Testing a Claim About a Standard Deviation or Variance.

1.97k views • 144 slides

Chapter 9 -Hypothesis Testing

Chapter 9 -Hypothesis Testing. Hypothesis testing can be used to determine whether a statement about the value of a population parameter should or should not be rejected. The null hypothesis , denoted by H 0 , is a tentative assumption about a population parameter.

556 views • 40 slides

1.21k views • 71 slides

chapter-7 hypothesis testing for quantitative variable

chapter-7 hypothesis testing for quantitative variable. contents. introduction Hypothesis testing 2.1 One sample t test 2.2 two independent-samples t test 2.3 Paired-samples t test. Hypothesis testing.

824 views • 82 slides

Chapter 6 Hypothesis Testing. What is Hypothesis Testing?. … the use of statistical procedures to answer research questions Typical research question (generic): For hypothesis testing, research questions are statements: This is the null hypothesis (assumption of “no difference”)

578 views • 57 slides

Chapter 9 Hypothesis Testing. Chapter Outline. Developing Null and Alternative Hypothesis Type I and Type II Errors Population Mean: Known Population Mean: Unknown Population Proportion. Hypothesis Testing.

630 views • 46 slides

Chapter 9 : Hypothesis Testing

Chapter 9 : Hypothesis Testing. Section 7 : Testing Differences of Two Means or Two Proportions (Independent Samples). Large Samples (Independent). Test Statistic. Test Statistic. , as stated in the null hypothesis

123 views • 9 slides

Chapter Seventeen HYPOTHESIS TESTING

Chapter Seventeen HYPOTHESIS TESTING. Approaches to Hypothesis Testing. Classical Statistics vs. Bayesian Approach Classical Statistics sampling-theory approach Making inference about a population based on sample evidence objective view of probability

149 views • 14 slides

• My presentations

Auth with social network:

Download presentation

We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!

Presentation is loading. Please wait.

Introduction to Hypothesis Testing

Published by Emily Payne Modified over 10 years ago

Similar presentations

Presentation on theme: "Introduction to Hypothesis Testing"— Presentation transcript:

Introductory Mathematics & Statistics for Business

Hypothesis Testing W&W, Chapter 9.

HYPOTHESIS TESTING. Purpose The purpose of hypothesis testing is to help the researcher or administrator in reaching a decision concerning a population.

Chapter 7 Hypothesis Testing

Our goal is to assess the evidence provided by the data in favor of some claim about the population. Section 6.2Tests of Significance.

Anthony Greene1 Simple Hypothesis Testing Detecting Statistical Differences In The Simplest Case:  and  are both known I The Logic of Hypothesis Testing:

Hypothesis Testing A hypothesis is a claim or statement about a property of a population (in our case, about the mean or a proportion of the population)

Statistical Issues in Research Planning and Evaluation

COURSE: JUST 3900 INTRODUCTORY STATISTICS FOR CRIMINAL JUSTICE Instructor: Dr. John J. Kerbs, Associate Professor Joint Ph.D. in Social Work and Sociology.

Hypothesis testing Week 10 Lecture 2.

HYPOTHESIS TESTING Four Steps Statistical Significance Outcomes Sampling Distributions.

Behavioural Science II Week 1, Semester 2, 2002

Evaluating Hypotheses Chapter 9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics.

Cal State Northridge  320 Ainsworth Sampling Distributions and Hypothesis Testing.

Evaluating Hypotheses Chapter 9 Homework: 1-9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics ~

Introduction to Hypothesis Testing CJ 526 Statistical Analysis in Criminal Justice.

Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 9-1 Chapter 9 Fundamentals of Hypothesis Testing: One-Sample Tests Basic Business Statistics.

PY 427 Statistics 1Fall 2006 Kin Ching Kong, Ph.D Lecture 6 Chicago School of Professional Psychology.

About project

© 2024 SlidePlayer.com Inc. All rights reserved.

• Preferences

RESEARCH HYPOTHESIS - PowerPoint PPT Presentation

RESEARCH HYPOTHESIS

Hypothesis should be testable & should not be a moral judgment. ... for example, roy s adaptation model is used in a research study, ... – powerpoint ppt presentation.

• A hypothesis is a formal tentative statement of the expected relationship between two or more variables under study.
• A hypothesis helps to translate the research problem objectives into a clear explanation or prediction of the expected results or outcomes of the research study.
• A clearly stated hypothesis includes the variables to be manipulated or measured, identifies the population to be examined, indicates the proposed outcome for the study.
• Hypothesis is a tentative prediction or explanation of the relationship between two variables. It implies that there is a systematic relationship between an independent a dependent variable.
• For example, dietary compliance will be greater in diabetic patients receiving diet instruction in small groups than in diabetic patients receiving individualized diet instructions.
• Good Hatt define hypothesis as a shrewd guess or inference that is formulated provisionally adopted to explain observed facts or conditions to guide in further investigation.
• Hypotheses enables the researcher to objectively investigate new areas of discovery. Thus , it provides a powerful tool for the advancement of knowledge.
• Hypotheses provides objectivity to the research activity.
• It also provides directions to conduct research such as defining the sources relevance of data.
• Hypotheses provides clear specific goals to the researchers. These clear specific goals provide the investigator with a basis for selecting sample research procedures to meet these goals.
• Hypotheses provides link between theories actual practical research.
• It provides a bridge between theory reality.
• A hypothesis suggests which type of research is likely to be most appropriate.
• As it is a tentative statement of anticipated results, it guides the researcher towards the direction in which the research should proceed.
• It stimulates the thinking process of researcher as the researcher forms the hypothesis by anticipating the outcome.
• It also determines the most appropriate research designs techniques of data analysis.
• Hypotheses provides understanding to the researchers about what expect from the results of the research study.
• It serves as framework for drawing conclusions of a research study.
• Without hypotheses, research would be like aimless wandering.
• Conceptual clarity
• Hypothesis should consist of clearly defined understandable concepts. It should be stated in very terms, the meaning implication of which cannot be doubted. To facilitate the conceptual clarity, hypothesis can be stated in declarative statement, in present tense.
• Empirical referents
• Research must have an ultimate empirical referent. No usable hypothesis can embody moral judgments. A good hypothesis must have empirical basis from the area of enquiry.
• Objectivity
• Hypothesis must be objective, which facilitates objectivity in data collection keeps the research activity free from researcher value - judgment.
• Specificity
• It should be specific, not general, should explain the expected relations between variables. For example, regular yoga reduces stress.
• The hypothesis should be relevant to the problem being studied as well as the objectives of the study. Hypothesis must have relevance with theory under test in a research process.
• Testability
• Hypothesis should be testable should not be a moral judgment. It must be directly/indirectly observable measurable. The researcher can set up a situation that permits one to assess if it is true or false. It must be verifiable. For example, a statement such as bad partners produce bad children. This sort of hypothesis cannot be tested.
• Consistency
• A hypothesis should be consistent with an existing body of theories, research findings, other hypotheses. It should correspond with existing knowledge.
• A hypothesis should be formulated in simple understandable terms. It should require fewer conditions assumptions.
• Availability of techniques
• The researchers must make sure that methods are available for testing their proposed hypotheses
• Purposiveness
• The researcher must formulate only purposeful hypotheses, which has relevance with research problem objectives.
• Verifiability
• A good hypothesis can be actually verified in practical terms.
• Profundity of effect
• A good hypothesis should have profound effect upon a variety of research variables.
• The expenditure of money the time can be controlled if the hypotheses underlying the research undertaken is good.
• The most important sources of hypotheses are theoretical or conceptual frameworks developed for the study.
• Through a deductive approach these hypotheses are drawn from theoretical or conceptual frameworks for testing them.
• For example, Roys adaptation Model is used in a research study, where a hypothesis can be drawn from a concept of the theoretical mode that patients adaptation to a chronic illness depends on availability of social support for them.
• Findings of the previous studies may be used for framing the hypotheses for another study.
• For example, in a small sample descriptive study, a researcher found that a number of patients admitted with coronary artery disease had increased body mass index.
• In another research study, a researcher may use this finding to formulate a hypothesis as Obese patients have increased risk for development of coronary artery disease.
• Real-life experiences also contribute in the formulation of hypotheses for research studies.
• For example, Newton had a life-changing experience of the falling of an apple formulated a hypothesis that earth attracts all the mass towards its centre, through several researchers were conducted before generating a law of central gravity.
• Academic literature is based on formal theories, empirical evidences, experiences, observation, conceptualizations of academicians.
• These literatures may serve as good sources for formulating hypotheses for research studies.
• Simple hypothesis
• It is a statement which reflects the relationship between two variables.
• For example, the lower the level of hemoglobin, the higher is the risk of infection among postpartum women.
• Complex hypothesis
• It is a statement which reflects the relationship between more than two variables.
• For example, satisfaction is higher among patients who are older dwelling in rural area than those who are younger dwelling in urban area.
• Associative hypothesis
• It reflects a relationship between variables that occurs or exists in natural settings without manipulation.
• This hypothesis is used in correlational research studies
• Causal hypothesis
• It predicts the cause-and-effect relationship between two or more dependent independent variables in experimental or interventional setting, where independent variable is manipulated by research to examine the effect on the dependent variable.
• The causal hypothesis reflects the measurement of dependent variable to examine the effect of dependent variable, which is manipulated by the researcher(s).
• For examples, prevalence of pin site infection is lower in patients who receive pin site care with hydrogen proxidide as compared to patients who receive the pin site care with Betadine solution.
• Directional hypothesis
• It specifies not only the existence, but also the expected direction of the relationship between variables.
• Directional hypothesis states the nature of the relationship between two or more variables such as positive, negative, or no relationship.
• To express the direction of relationship between variables, the directional terms are used to state the hypothesis such as positive, negative, less, more, increased, decreased, greater, higher, lower, etc.
• For examples, there is a positive relationship between years of nursing experience job satisfaction among nurses.
• Nondirectional Hypothesis
• It reflects the relationship between two or more variables, but is does not specify the anticipated direction nature of relationship such as positive or negative.
• It indicates the existence of relationship between the variables.
• For example, there is relationship between years of nursing experience job satisfaction among nurses.
• Null hypothesis (H0)
• It is also known as statistical hypothesis is used for statistical testing interpretation of statistical outcomes.
• It states the existence of no relationship between the independent dependent variables.
• For example, there is no relationship between smoking the incidence of coronary artery disease.
• Research hypothesis (H1)
• It states the existence of relationship between two or more variables.
• For examples, there is relationship between smoking incidence of lung cancer.

PowerShow.com is a leading presentation sharing website. It has millions of presentations already uploaded and available with 1,000s more being uploaded by its users every day. Whatever your area of interest, here you’ll be able to find and view presentations you’ll love and possibly download. And, best of all, it is completely free and easy to use.

You might even have a presentation you’d like to share with others. If so, just upload it to PowerShow.com. We’ll convert it to an HTML5 slideshow that includes all the media types you’ve already added: audio, video, music, pictures, animations and transition effects. Then you can share it with your target audience as well as PowerShow.com’s millions of monthly visitors. And, again, it’s all free.

About the Developers

PowerShow.com is brought to you by  CrystalGraphics , the award-winning developer and market-leading publisher of rich-media enhancement products for presentations. Our product offerings include millions of PowerPoint templates, diagrams, animated 3D characters and more.

Home Blog Education How to Prepare Your Scientific Presentation

How to Prepare Your Scientific Presentation

Since the dawn of time, humans were eager to find explanations for the world around them. At first, our scientific method was very simplistic and somewhat naive. We observed and reflected. But with the progressive evolution of research methods and thinking paradigms, we arrived into the modern era of enlightenment and science. So what represents the modern scientific method and how can you accurately share and present your research findings to others? These are the two fundamental questions we attempt to answer in this post. 

What is the Scientific Method?

To better understand the concept, let’s start with this scientific method definition from the International Encyclopedia of Human Geography :

The scientific method is a way of conducting research, based on theory construction, the generation of testable hypotheses, their empirical testing, and the revision of theory if the hypothesis is rejected. 

Essentially, a scientific method is a cumulative term, used to describe the process any scientist uses to objectively interpret the world (and specific phenomenon) around them. 

The scientific method is the opposite of beliefs and cognitive biases — mostly irrational, often unconscious, interpretations of different occurrences that we lean on as a mental shortcut. 

The scientific method in research, on the contrary, forces the thinker to holistically assess and test our approaches to interpreting data. So that they could gain consistent and non-arbitrary results. 

The common scientific method examples are:

• Systematic observation 
• Experimentation
• Inductive and deductive reasoning
• Formation and testing of hypotheses and theories

All of the above are used by both scientists and businesses to make better sense of the data and/or phenomenon at hand. 

The Evolution of the Scientific Method 

According to the Stanford Encyclopedia of Philosophy , ancient thinkers such as Plato and Aristotle are believed to be the forefathers of the scientific method. They were among the first to try to justify and refine their thought process using the scientific method experiments and deductive reasoning. 

Both developed specific systems for knowledge acquisition and processing. For example, the Platonic way of knowledge emphasized reasoning as the main method for learning but downplayed the importance of observation. The Aristotelian corpus of knowledge, on the contrary, said that we must carefully observe the natural world to discover its fundamental principles. 

In medieval times, thinkers such as Thomas Aquinas, Roger Bacon, and Andreas Vesalius among many others worked on further clarifying how we can obtain proven knowledge through observation and induction. 

The 16th–18th centuries are believed to have given the greatest advances in terms of scientific method application. We, humans, learned to better interpret the world around us from mechanical, biological, economic, political, and medical perspectives. Thinkers such as Galileo Galilei, Francis Bacon, and their followers also increasingly switched to a tradition of explaining everything through mathematics, geometry, and numbers. 

Up till today, mathematical and mechanical explanations remain the core parts of the scientific method. 

Why is the Scientific Method Important Today? 

Because our ancestors didn’t have as much data as we do. We now live in the era of paramount data accessibility and connectivity, where over 2.5 quintillions of data are produced each day. This has tremendously accelerated knowledge creation.

But, at the same time, such overwhelming exposure to data made us more prone to external influences, biases, and false beliefs. These can jeopardize the objectivity of any research you are conducting. 

Scientific findings need to remain objective, verifiable, accurate, and consistent. Diligent usage of scientific methods in modern business and science helps ensure proper data interpretation, results replication, and undisputable validity. 

6 Steps of the Scientific Method

Over the course of history, the scientific method underwent many interactions. Yet, it still carries some of the integral steps our ancestors used to analyze the world such as observation and inductive reasoning. However, the modern scientific method steps differ a bit. 

1. Make an Observation 

An observation serves as a baseline for your research. There are two important characteristics for a good research observation:

• It must be objective, not subjective. 
• It must be verifiable, meaning others can say it’s true or false with this. 

For example, This apple is red (objective/verifiable observation). This apple is delicious (subjective, harder-to-verify observation).

2. Develop a Hypothesis

Observations tell us about the present or past. But the goal of science is to glean in the future. A scientific hypothesis is based on prior knowledge and produced through reasoning as an attempt to descriptive a future event.

Here are characteristics of a good scientific hypothesis: 

• General and tentative idea
• Agrees with all available observations
• Testable and potentially falsifiable

Remember: If we state our hypothesis to indicate there is no effect, our hypothesis is a cause-and-effect relationship . A hypothesis, which asserts no effect, is called a null hypothesis. 

3. Make a Prediction 

A hypothesis is a mental “launchpad” for predicting the existence of other phenomena or quantitative results of new observations.

Going back to an earlier example here’s how to turn it into a hypothesis and a potential prediction for proving it. For example: If this apple is red, other apples of this type should be red too. 

Your goal is then to decide which variables can help you prove or disprove your hypothesis and prepare to test these. 

4. Perform an Experiment 

Collect all the information around variables that will help you prove or disprove your prediction. According to the scientific method, a hypothesis has to be discarded or modified if its predictions are clearly and repeatedly incompatible with experimental results.

Yes, you may come up with an elegant theory. However, if your hypothetical predictions cannot be backed by experimental results, you cannot use them as a valid explanation of the phenomenon. 

5. Analyze the Results of the Experiment

To come up with proof for your hypothesis, use different statistical analysis methods to interpret the meaning behind your data.

Remember to stay objective and emotionally unattached to your results. If 95 apples turned red, but 5 were yellow, does it disprove your hypothesis? Not entirely. It may mean that you didn’t account for all variables and must adapt the parameters of your experiment. 

Here are some common data analysis techniques, used as a part of a scientific method: 

• Statistical analysis
• Cause and effect analysis (see cause and effect analysis slides )
• Regression analysis
• Factor analysis
• Cluster analysis
• Time series analysis
• Diagnostic analysis
• Root cause analysis (see root cause analysis slides )

6. Draw a Conclusion 

Every experiment has two possible outcomes:

• The results correspond to the prediction
• The results disprove the prediction 

If that’s the latter, as a scientist you must discard the prediction then and most likely also rework the hypothesis based on it. 

How to Give a Scientific Presentation to Showcase Your Methods

Whether you are doing a poster session, conference talk, or follow-up presentation on a recently published journal article, most of your peers need to know how you’ve arrived at the presented conclusions.

In other words, they will probe your scientific method for gaps to ensure that your results are fair and possible to replicate. So that they could incorporate your theories in their research too. Thus your scientific presentation must be sharp, on-point, and focus clearly on your research approaches. 

Below we propose a quick framework for creating a compelling scientific presentation in PowerPoint (+ some helpful templates!). 

1. Open with a Research Question 

Here’s how to start a scientific presentation with ease: share your research question. On the first slide, briefly recap how your thought process went. Briefly state what was the underlying aim of your research: Share your main hypothesis, mention if you could prove or disprove them. 

It might be tempting to pack a lot of ideas into your first slide but don’t. Keep the opening of your presentation short to pique the audience’s initial interest and set the stage for the follow-up narrative.

2. Disclose Your Methods

Whether you are doing a science poster presentation or conference talk, many audience members would be curious to understand how you arrived at your results. Deliver this information at the beginning of your presentation to avoid any ambiguities. 

Here’s how to organize your science methods on a presentation: 

• Do not use bullet points or full sentences. Use diagrams and structured images to list the methods
• Use visuals and iconography to use metaphors where possible.
• Organize your methods by groups e.g. quantifiable and non-quantifiable

Finally, when you work on visuals for your presentation — charts, graphs, illustrations, etc. — think from the perspective of a subject novice. Does the image really convey the key information around the subject? Does it help break down complex ideas?

3. Spotlight the Results 

Obviously, the research results will be your biggest bragging right. However, don’t over-pack your presentation with a long-winded discussion of your findings and how revolutionary these may be for the community. 

Rather than writing a wall of text, do this instead:

• Use graphs with large axis values/numbers to showcase the findings in great detail
• Prioritize formats that are known to everybody (e.g. odds ratios, Kaplan Meier curves, etc.)
• Do not include more than 5 lines of plain text per slide 

Overall, when you feel that the results slide gets too cramped, it’s best to move the data to a new one. 

Also, as you work on organizing data on your scientific presentation PowerPoint template , think if there are obvious limitations and gaps. If yes, make sure you acknowledge them during your speech.

4. Mention Study Limitations 

The scientific method mandates objectivity. That’s why every researcher must clearly state what was excluded from their study. Remember: no piece of scientific research is truly universal and has certain boundaries. However, when you fail to personally state those, others might struggle to draw the line themselves and replicate your results. Then, if they fail to do so, they’d question the viability of your research.

5. Conclude with a Memorable Takeaway Message 

Every experienced speaker will tell you that the audience best retains the information they hear first and last. Most people will attend more than one scientific presentation during the day. 

So if you want the audience to better remember your talk, brainstorm a take-home message for the last slide of your presentation. Think of your last slide texts as an elevator pitch — a short, concluding message, summarizing your research.

To Conclude

Today we have no shortage of research and scientific methods for testing and proving our hypothesis. However, unlike our ancestors, most scientists experience deeper scrutiny when it comes to presenting and explaining their findings to others. That’s why it’s important to ensure that your scientific presentation clearly relays the aim, vector, and thought process behind your research.

Like this article? Please share

Education, Presentation Ideas, Presentation Skills, Presentation Tips Filed under Education

Related Articles

Filed under Google Slides Tutorials • May 3rd, 2024

How to Work with Google Slides Version History

Go back to previous changes or check who edited your presentation. Learn how to work with Google Slides Version History here.

Filed under Design , Presentation Ideas • May 1st, 2024

The Power of Mind Map Note Taking for Presenters

Add a new tool to your repertoire of presentation skills by mastering the art of mind map note taking. An ideal process to facilitate content retention.

Filed under Google Slides Tutorials • April 29th, 2024

Best Google Slides Add-Ons

Optimize your Google Slides experience by installing the best Google Slides add-ons available in the market. Full list with photos.

Leave a Reply

Got any suggestions?

We want to hear from you! Send us a message and help improve Slidesgo

Top searches

Trending searches

11 templates

9 templates

art portfolio

79 templates

holy spirit

36 templates

32 templates

human anatomy

18 templates

Research Presentation templates

Customize our free themes and templates for google slides or powerpoint and explain what your research is about. these designs are easy to edit, so that will speed things up.

Premium template

Unlock this template and gain unlimited access

Research Project Proposal

Before embarking yourself on a new project, especially if it’s about research, you need to set out a proposal to explain its viability. Here at Slidesgo we’re offering this theme that you can actually use for any kind of project, regardless of the topic.

Formal Research Paper Slideshow

Have you seen these slides? They are perfect for presenting your research paper! First of all, because we have included all the necessary sections of this type of work, such as hypothesis, objectives, methodology, analysis and the conclusions of the paper. The second reason is that the formal style will...

Research Methods Lesson

If you deal with Science, it’s important to learn more about research methods. Teach your students about them with this presentation full of illustrations and drawings related to labs. Use graphs, maps, tables and overview diagrams to support your lecture in a visual way!

Heart Valve Replacement Surgery Case Report

Download the Heart Valve Replacement Surgery Case Report presentation for PowerPoint or Google Slides. A clinical case is more than just a set of symptoms and a diagnosis. It is a unique story of a patient, their experiences, and their journey towards healing. Each case is an opportunity for healthcare...

Nursing Capstone

In medical contexts, a capstone is often the final course in a nursing degree, a project of vital importance. It’s very demanding, so if you need help with the presentation, use this free professional template. Leave the design to us and focus on your data!

Economics Thesis

If numbers, exchange rates, money and trading are your forte, odds are you’re already working on an economics thesis for your master’s degree. Defending your dissertation is the last step and the most difficult one, but Slidesgo can help you. Here’s our new free presentation template with a focus on...

Cardiovascular Diseases: Arrhythmia Infographics

The Vaccine Revolt Thesis Defense

Download the The Vaccine Revolt Thesis Defense presentation for PowerPoint or Google Slides. Congratulations, you have finally finished your research and made it to the end of your thesis! But now comes the big moment: the thesis defense. You want to make sure you showcase your research in the best...

Climacteric Syndrome Case Study

Download the Climacteric Syndrome Case Study presentation for PowerPoint or Google Slides. A clinical case is more than just a set of symptoms and a diagnosis. It is a unique story of a patient, their experiences, and their journey towards healing. Each case is an opportunity for healthcare professionals to...

9th Century Thesis Defense

Download the 9th Century Thesis Defense presentation for PowerPoint or Google Slides. Whether you're an entrepreneur looking for funding or a sales professional trying to close a deal, a great pitch deck can be the difference-maker that sets you apart from the competition. Let your talent shine out thanks to...

Pregnancy Breakthrough

Giving birth to a baby is a beautiful occasion, a manifestation of love between two people. Obstetrics are key during pregnancy, so how about giving a presentation about the latest breakthrough in this field? Our free medical template will come in handy.

The Role of the Cangaço Thesis Defense

Download the The Role of the Cangaço Thesis Defense presentation for PowerPoint or Google Slides. Congratulations, you have finally finished your research and made it to the end of your thesis! But now comes the big moment: the thesis defense. You want to make sure you showcase your research in...

Documentary Journalist Portfolio

Download the Documentary Journalist Portfolio presentation for PowerPoint or Google Slides. When a potential client or employer flips through the pages of your portfolio, they're not just looking at your work; they're trying to get a sense of who you are as a person. That's why it's crucial to curate...

Project Research Infographics

Download the "Project Research Infographics" template for PowerPoint or Google Slides and discover the power of infographics. An infographic resource gives you the ability to showcase your content in a more visual way, which will make it easier for your audience to understand your topic. Slidesgo infographics like this set...

Elegant Black & White Thesis Defense

Present your research findings with grace and assertiveness through this template. Available for Google Slides and PowerPoint, this design set offers minimalistic charm with its simple, gray scale elegance. The template not only provides a polished platform to showcase your thesis but also ensures seamless and efficient delivery of your...

AP Research Defense for High School

AP, or Advanced Placement, is a North American educational program that offers a rigorous course designed to challenge and prepare high school students for their future careers and academic pursuits. It requires students to conduct independent research, write a lengthy academic paper, and present their findings to a panel of...

Data Analysis for Marketing Strategies

With the amount of data available through various digital platforms, it's easier than ever to determine the trends and preferences of your target audience. By collecting and analyzing data, marketers can create highly personalized campaigns that align with the exact needs and wants of their customers. If you're trying to...

SWOT Analysis Infographics

Discover the strengths, weaknesses, opportunities and threats of your own company performing a SWOT analysis. Use this basic strategic planning to evaluate your position with these new infographics created by Slidesgo.

• Page 1 of 87

Great presentations, faster

Slidesgo for Google Slides :

The easy way to wow

Register for free and start editing online