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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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Margin of Error01:27

Margin of Error

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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

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A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
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Related Experiment Video

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Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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[How many subjects do I need to power my study?].

Sergio R Muñoz Navarro1

  • 1Departamento de Salud Pública – CIGES, Facultad de Medicina, Universidad de la Frontera, Chile. Address: Montt #112, Temuco, Chile.

Medwave
|October 30, 2014
PubMed
Summary

Determining the correct sample size is crucial for the statistical power of epidemiological studies. This tool and the Epidat software demonstrate sample size calculations for prevalence, case-control, and cohort studies.

Area of Science:

  • Epidemiology
  • Biostatistics

Context:

  • Sample size determination is essential for robust observational epidemiological research.
  • Accurate sample size ensures adequate statistical power to detect significant associations.
  • Epidemiological studies require careful planning, including sample size estimation.

Purpose:

  • To present a tool and methodology for calculating sample size in observational epidemiological studies.
  • To demonstrate the application of sample size calculations using the Epidat statistical package.
  • To provide practical examples for common study designs like prevalence, case-control, and cohort studies.

Summary:

  • The article introduces a method for determining the necessary sample size for epidemiological studies.
  • It details sample size calculations for cross-sectional, case-control, and cohort study designs.

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  • The statistical software Epidat is used to illustrate these calculations with practical examples.

    • Impact:

    • Facilitates accurate sample size determination, enhancing the reliability of epidemiological research findings.
    • Provides a practical resource for researchers using the Epidat software for study design.
    • Contributes to improving the quality and efficiency of observational epidemiological studies.