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Sample Size Calculation

Sample Size Calculation

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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Margin of Error

Margin of Error

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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Related Experiment Video

Updated: May 7, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Bayesian sample size determination for case-control studies when exposure may be misclassified.

Lawrence Joseph, Patrick Bélisle

American Journal of Epidemiology

|September 17, 2013

Summary

This summary is machine-generated.

Accurate sample size calculation for case-control studies is crucial, especially when exposure data may be misclassified. This study provides Bayesian methods to determine sample sizes, accounting for exposure misclassification to improve estimate precision.

Keywords:

Bayesian methods case-control study misclassification error sample size determination study design

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

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Published on: January 8, 2020

Area of Science:

Epidemiology
Biostatistics

Background:

Odds ratios are key for estimating exposure effects on disease in case-control studies.
Exposure misclassification, due to recall bias or measurement error, can significantly impact study accuracy and precision.
Existing methods for sample size determination often overlook the impact of anticipated exposure misclassification.

Purpose of the Study:

To develop and present methods for sample size determination in case-control studies that account for exposure misclassification.
To provide a framework for ensuring sufficient accuracy and precision of odds ratio estimation when exposure data is imperfect.

Main Methods:

Utilizing interval-based Bayesian criteria for sample size calculations.
Developing methods to adjust sample size requirements for anticipated exposure misclassification.
Comparing sample size needs with and without adjustment for misclassification using prototypical examples.

Main Results:

The study demonstrates that accounting for exposure misclassification leads to different sample size requirements compared to ignoring it.
The proposed Bayesian methods offer a way to determine adequate sample sizes under realistic data quality conditions.
Sample size adjustments are necessary to maintain desired precision in the presence of misclassification.

Conclusions:

Accurate sample size determination in case-control studies must consider potential exposure misclassification.
The developed Bayesian approach provides a robust method for planning studies with imperfect exposure data.
Applying these methods can lead to more precise and reliable estimation of odds ratios in epidemiologic research.