Figure 5: The Green Stink Bug ( Nezara viridula ) assumes different body forms through metamorphosis between different discrete life stages. Photo courtesy of Jovo26 via Wikimedia Commons
Interestingly, sex ratio is not always random but can be manipulated at birth by environmental or physiological mechanisms. All crocodiles and many reptiles utilize a strategy called environmental sex determination, wherein incubation temperature determines the sex of each individual (Delmas et al . 2008). For example, low temperatures will produce males and high temperatures will produce females. In times of limited resources or high population densities, females can manipulate the sex ratios of their clutch by spending more or less time incubating their eggs (Girondot et al . 2004).
age-specific : The age of the individual is important for statistical purposes.
clutch size : The number of offspring one female produces in one reproductive cycle.
cohort : Group of all individuals sharing a statistical factor (such as age or developmental stage)
density-dependent factors : Depending on the local density of the population
density-independent factors : Not linked to the local density of the population
discrete developmental stages : Non-overlapping and structurally distinct growth stages. E.g. tadpoles are one discrete developmental stage and adult frogs are another.
ecosystem : A natural system including the interaction of all living and non-living elements.
extinction : No longer existing.
extrapolating : Estimating an unknown value by assuming that a known value can translate (without distortion) to the scale of the unknown value.
growth rate : The rate of change of population size over time.
inbreeding : Breeding of closely related individuals, often with negative genetic consequences.
incubated : Provided with a heat source during embryonic development.
life tables : Specific format of statistical summary of demographic parameters.
migration : Populations moving from one geographic location to another.
objectively : To study without bias and by measurable and repeatable metrics.
offspring : The individual produced from the reproduction of its parents.
parameter : A value in an equation that does not vary. These values can change between different equations of similar form.
predator-prey relationships : How populations of predators are interacting with populations of prey.
predation : The act of killing another living organism for food.
physiological : The parts and functions of living organisms.
reproductive organs : Specialized collection of cells used to exchange gametes between sexually reproducing organisms.
rates : A mathematical term for the number of things or events happening in a given amount of time.
rearing : To invest energy in the growth and development of offspring after they are born.
subjectively designated geographic range : A parcel of land, the size of which is chosen without using standardized criteria. Picked at the discretion of the researcher.
sustainable : System able to be maintained itself indefinitely without supplement.
sexual maturation : An individual reaching a stage of development where it is able to sexually reproduce.
stress hormone : Chemical compounds synthesized in the body to chemically communicate a stress reaction to various systems within that organism.
statistic : A number acting as a description for more numbers.
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Girondot, M. et al . "Implications of temperature-dependent sex determination for population dynamics," Temperature-Dependent Sex Determination in Vertebrates , 148-155, eds. N. Valenzuela & V. Lance. Smithsonian Books, 2004.
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Hutchinson, G. E. Population studies: Animal ecology and demography. Bulletin of Mathematical Biology 53, 193-213 (1991).
Jiguet, F. et al . Bird population trends are linearly affected by climate change along species thermal ranges. Proceedings of the Royal Society B: Biological Sciences 277, 3601-3608 (2010).
Krohne, D. T., Dubbs, B. A. & Baccus, R. An analysis of dispersal in an unmanipulated population of Peromyscus leucopus. American Midland Naturalist 112, 146-156 (1984).
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Published on 3 May 2022 by Pritha Bhandari . Revised on 5 December 2022.
A population is the entire group that you want to draw conclusions about.
A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.
In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organisations, countries, species, or organisms.
Collecting data from a population, collecting data from a sample, population parameter vs sample statistic, practice questions: populations vs samples, frequently asked questions about samples and populations.
Populations are used when your research question requires, or when you have access to, data from every member of the population.
Usually, it is only straightforward to collect data from a whole population when it is small, accessible and cooperative.
For larger and more dispersed populations, it is often difficult or impossible to collect data from every individual. For example, every 10 years, the federal US government aims to count every person living in the country using the US Census. This data is used to distribute funding across the nation.
However, historically, marginalised and low-income groups have been difficult to contact, locate, and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate funding across the country.
In cases like this, sampling can be used to make more precise inferences about the population.
When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sample. With statistical analysis , you can use sample data to make estimates or test hypotheses about population data.
Ideally, a sample should be randomly selected and representative of the population. Using probability sampling methods (such as simple random sampling or stratified sampling ) reduces the risk of sampling bias and enhances both internal and external validity .
For practical reasons, researchers often use non-probability sampling methods . Non-probability samples are chosen for specific criteria; they may be more convenient or cheaper to access. Because of non-random selection methods, any statistical inferences about the broader population will be weaker than with a probability sample.
When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data. A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample.
You can use estimation or hypothesis testing to estimate how likely it is that a sample statistic differs from the population parameter.
A sampling error is the difference between a population parameter and a sample statistic. In your study, the sampling error is the difference between the mean political attitude rating of your sample and the true mean political attitude rating of all undergraduate students in the Netherlands.
Sampling errors happen even when you use a randomly selected sample. This is because random samples are not identical to the population in terms of numerical measures like means and standard deviations .
Because the aim of scientific research is to generalise findings from the sample to the population, you want the sampling error to be low. You can reduce sampling error by increasing the sample size.
Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.
Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.
A statistic refers to measures about the sample , while a parameter refers to measures about the population .
A sampling error is the difference between a population parameter and a sample statistic .
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Population research is a scientific inquiry to understand the population dynamics of a population’s size, structure, growth, distribution, and dispersal.
In contrast, family planning research inquires about contraception, side effects, follow-up, etc.
Reproductive health encompasses maternal, adolescent, antenatal, postnatal, and delivery care. Child health includes, among others, nutrition, growth monitoring, breastfeeding, immunization, diarrhoeal diseases, etc.
Example #1: (Population dynamics)
Kabir et al. (1997), utilizing the data from various sources, examined the prospect of stabilization of the Bangladesh population under alternative demographic scenarios.
They further discussed opportunities for achieving zero population growth vis a vis NRR=1 by the end of 2010. Their findings concluded that if the fertility target could be completed as envisaged, the Bangladesh population would be more or less stable by 2050.
Example #2: (Population/Demography)
While substantial fertility decline has started to take place in other countries of the South Asian region, Bangladesh has shown only a slight decrease in the prevailing high fertility rates.
Several demographers, economists, and sociologists have emphasized the role of demand for children as an essential source of change in the reproductive behavior of individuals (Bulatao, 1981; Pullum, 1983; Bulatao and Lee, 1983; Pritchett, 1994).
In Bangladesh, many social scientists believe that the demand for children is still high, which keeps fertility levels high; couples prefer to have more children.
It is thus imperative to have an insight into the fertility preferences maintained by the people that are considered to have an essential bearing on fertility outcomes and contraceptive use behavior.
Example #3: (Family Planning)
Duston and Miller (1995) initiated research to ascertain how to improve community-based family planning services and the potential for increasing contraceptive prevalence in Bangladesh.
The study’s specific objectives were to investigate the degree to which improved service delivery in Bangladesh can increase contraceptive use given the present status of demand and programmatic factors most associated with increased prevalence and make these projects viable and more widely known.
The National Institute of Population Research and Training (NIPORT), under the Ministry of Health and Family Welfare, Govt of Bangladesh, contacts some studies encompassing health, nutrition, family planning, and reproductive health.
In a recent search for priority research, NIPORT identified a few areas and prepared a list to execute the studies in 2017-18.
While population research focuses on understanding population dynamics, family planning research specifically inquires about topics like contraception, side effects, follow-up, and other related aspects.
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In statistics , a population is the pool from which a sample is drawn for a study. Thus, any selection grouped by a common feature can be considered a population. A sample is a statistically significant portion of a population.
Statisticians, scientists, and analysts prefer to know the characteristics of every entity in a population to draw the most precise conclusions possible. However, this is impossible or impractical most of the time since population sets tend to be quite large. A sample of a population must usually be taken since the characteristics of every individual in a population cannot be measured due to constraints of time, resources, and accessibility.
It's important to note that when referring to an individual in statistics, the term does not always mean a person. Statistically, an individual is a single entity in the group being studied.
For example, there is no real way to gather data on all of the great white sharks in the ocean (a population) because finding and tagging each one isn't feasible. So, marine biologists tag the great whites they can (a sample) and begin collecting information on them to make inferences about the entire population of great whites. This is a random sampling approach because the initial encounters with tagged great whites are entirely random.
A valid statistic may be drawn from either a sample or a study of an entire population. The objective of a random sample is to avoid bias in the results. A sample is random if every member of the whole population has an equal chance to be selected to participate.
The difficulty of measuring a population lies in whatever you're attempting to analyze and what you're trying to accomplish. Data must be collected through surveys, measurements, observation, or other methods.
Therefore, gathering the data on a large population is generally not done because of the costs, time, and resources necessary to obtain it. For instance, when you see advertisements claiming, "62% of doctors recommend XYZ for their patients,"—all of the doctors with patients who could use XYZ in the U.S. were likely not contacted. Of the doctors who responded to the several hundred or thousand surveys that were requested, 62% responded that they would recommend XYZ—this is a population sample.
While a parameter is a characteristic of a population, a statistic is a characteristic of a sample, and samples can only result in inferences about a population characteristic. Inferential statistics enables you to make an educated guess about a population parameter based on a statistic computed from a sample randomly drawn from that population.
Statistics such as averages (means) and standard deviations , when taken from populations, are referred to as population parameters. Many, such as a population's mean and standard deviation, are represented by Greek letters like µ (mu) and σ (sigma). Much of the time, these statistics are inferential in nature because samples are used rather than populations.
If you have all the data for the population being studied, you do not need to use statistical inference because you won't need to use a sample of the population.
Market and investment analysts use statistics to analyze investment data and make inferences about the market, a specific investment, or an index. In some cases, financial analysts can evaluate an entire population because price data has been recorded for decades. For example, the price of every publicly traded stock could be analyzed for a total market evaluation because the prices are recorded—this is a population, in terms of investment analysis. Another population might be the stock prices of all tech companies since 2010.
An analyst can calculate parameters with all of this data; however, the parameters used by analysts are only occasionally used in the same way statisticians and scientists use them.
Some of the parameters you might see used by investment analysts, statisticians, and scientists and their differences are:
Alpha : The excess returns of an asset compared to a benchmark
Standard Deviation : Average amount of variability in prices, used to measure volatility and risk
Moving Average : Used to smooth out short-term price fluctuations to indicate trends
Beta : Measures the performance of an investment/portfolio against the market as a whole
Alpha : The probability of making a Type I error, or rejecting the null hypothesis when it is true
Standard Deviation : Average amount of variablility in data
Moving Average : Smooths out short-term fluctuations in data values
Beta : The probability of making a Type II error, or incorrectly failing to reject the null hypothesis
A population mean is the average of whatever value you're measuring in a given population.
One example of a population might be all green-eyed children in the U.S. under age 12. Another could be all the great white sharks in the ocean.
Imagine you're a teacher trying to see how well your fifth-grade math class did on a standardized test compared to all fifth-graders in the U.S. The population would be all fifth-grade math scores in the country.
In statistics, a population is the pool being studied from which data is extracted. Populations can be difficult to gather data on, especially if the studied topic is expansive and widely dispersed. Studying humans is an excellent example—there is no way to gather data on every brown-eyed person in the world (a statistical population), so random sampling is the only way to infer anything about that population.
In investment analysis, populations are generally specific types of assets being analyzed. These data sets are generally small (in statistical terms) and easy to acquire because they have been recorded, unlike data on living organisms, which is much more difficult to obtain.
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A population is a complete set of people with specified characteristics, while a sample is a subset of the population. 1 In general, most people think of the defining characteristic of a population in terms of geographic location. However, in research, other characteristics will define a population.
1. Define the Population. Clearly define the target population for your research study. The population should encompass the group of individuals, elements, or units that you want to draw conclusions about. 2. Define the Sampling Frame. Create a sampling frame, which is a list or representation of the individuals or elements in the target ...
A population is the entire group that you want to draw conclusions about.. A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population. In research, a population doesn't always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries ...
A population is a complete set of people with a specialized set of characteristics, and a sample is a subset of the population. The usual criteria we use in defining population are geographic, for example, "the population of Uttar Pradesh". In medical research, the criteria for population may be clinical, demographic and time related.
Research Population. All research questions address issues that are of great relevance to important groups of individuals known as a research population. A research population is generally a large collection of individuals or objects that is the main focus of a scientific query. It is for the benefit of the population that researches are done.
In research, there are 2 kinds of populations: the target pop-ulation and the accessible population. The accessible popula-tion is exactly what it sounds like, the subset of the target population that we can easily get our hands on to conduct our research. While our target population may be Caucasian females with a GFR of 20 or less who are ...
The defined population then will become the basis for applying the research results to other relevant populations. Clearly defining a study population early in the research process also helps assure the overall validity of the study results. Many research reports fail to define or describe a study population adequately.
Definition. Study population is a subset of the target population from which the sample is actually selected. It is broader than the concept sample frame. It may be appropriate to say that sample frame is an operationalized form of study population. For example, suppose that a study is going to conduct a survey of high school students on their ...
Define the population of interest and; Define the question to be answered; These requirements go hand-in-hand, because selection of an appropriate study population is dependent upon the question being addressed. Sometimes the study population seems obvious given the research question, but the study populations may be broader than that which at ...
Methods. In this article, I review the current conventional definitions of, and historical debates over, the meaning(s) of "population," trace back the contemporary emphasis on populations as statistical rather than substantive entities to Adolphe Quetelet's powerful astronomical metaphor, conceived in the 1830s, of l'homme moyen (the average man), and argue for an alternative definition ...
Answers Chapter 3 Q3.pdf. Populations In statistics the term "population" has a slightly different meaning from the one given to it in ordinary speech. It need not refer only to people or to animate creatures - the population of Britain, for instance or the dog population of London. Statisticians also speak of a population.
The first step in addressing the population in research is to clearly define the target population. This involves specifying the characteristics of the larger group to which the study's findings will be generalized. The target population should be explicitly defined in terms of relevant factors such as demographic characteristics, geographic ...
After defining the research question, a study must identify the study population to assess. Study populations can include a whole target population (i.e., census); however, most studies include sampling, in which the sample represents a subset of the target population. ... cohort studies may define a population by an exposure/intervention ...
Aa Aa Aa. A population is defined as a group of individuals of the same species living and interbreeding within a given area. Members of a population often rely on the same resources, are subject ...
A population is the entire group that you want to draw conclusions about. A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population. In research, a population doesn't always refer to people. It can mean a group containing elements of anything you want to study ...
The sampling frame intersects the target population. The sam-ple and sampling frame described extends outside of the target population and population of interest as occa-sionally the sampling frame may include individuals not qualified for the study. Figure 1. The relationship between populations within research.
design, population of interest, study setting, recruit ment, and sampling. Study Design. The study design is the use of e vidence-based. procedures, protocols, and guidelines that provide the ...
Examples of Population Research. Example #1: (Population dynamics) Kabir et al. (1997), utilizing the data from various sources, examined the prospect of stabilization of the Bangladesh population under alternative demographic scenarios. They further discussed opportunities for achieving zero population growth vis a vis NRR=1 by the end of 2010.
Tell your students that you will read a scenario and they must decide on whether the research scenario relates to a population or a sample. If it is a sample, they must identify the type of sample ...
Learn how to define and differentiate the population and the target population in research studies with this informative article. Download the PDF for free on ResearchGate.
Population is the entire pool from which a statistical sample is drawn. In statistics, population may refer to people, objects, events, hospital visits, measurements, etc. A population can ...
This review emphasizes the importance of clarifying terminology and adopting a systematic approach to population definition, considering demographic characteristics, clinical parameters, and ...
The term is not well known among the population it is meant to describe. In a 2019 Center survey, only 23% of U.S. adults who self-identified as Hispanic or Latino had heard of the term, and just 3% said they use it to describe themselves. However, awareness and use of the term varied across subgroups of Hispanics.
The population refers to an entire set of units that exhibit a variable characteristic under investigation and for which research findings can be generalized (Shukla, 2020). Meanwhile, a sample is ...