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Related Concept Videos

Sample Size Calculation01:19

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...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...

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Related Experiment Video

Updated: Jun 27, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Sample size calculations for evaluating mediation.

E Vittinghoff1, S Sen, C E McCulloch

  • 1Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA. eric@biostat.ucsf.edu

Statistics in Medicine
|December 10, 2008
PubMed
Summary
This summary is machine-generated.

This study provides methods for calculating sample sizes needed to test mediation effects. These techniques are crucial for validating surrogate markers and analyzing primary predictor effects with confounders.

Related Experiment Videos

Last Updated: Jun 27, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Testing mediation effects is essential in various research areas, including validating surrogate markers and analyzing primary predictor effects in the presence of confounders.
  • Existing literature offers sample size calculation methods for related problems that can be adapted for mediation analysis.
  • Accurate sample size determination is critical for the statistical power and validity of mediation studies.

Purpose of the Study:

  • To present and evaluate methods for sample size calculations specifically for testing mediation effects.
  • To adapt existing sample size calculation proposals for mediation analysis in different statistical models.
  • To introduce new analytical and simulation-based procedures for sample size determination in mediation studies.

Main Methods:

  • Utilizing variance inflation factor (VIF) methods for exact sample size calculations in linear regression and approximations for logistic, Poisson, and Cox models.
  • Developing a simulation-based procedure for sample size calculations applicable to logistic and Cox models.
  • Proposing a novel analytical method for sample size calculations in Poisson models.
  • Conducting simulation studies to investigate the performance of the different proposed methods.

Main Results:

  • The variance inflation factor method provides exact calculations for linear models and approximations for generalized linear and survival models.
  • Simulation-based and new analytical methods offer viable alternatives for sample size calculations in logistic, Poisson, and Cox models.
  • Simulation studies demonstrated the behavior and performance of the various sample size calculation approaches.

Conclusions:

  • The proposed methods offer practical tools for researchers to determine appropriate sample sizes for mediation analysis.
  • The study provides a comprehensive comparison of different sample size calculation strategies across various statistical models.
  • These findings contribute to robust study design and reliable statistical inference in mediation research.