Sample Size Calculator

Calculate required sample size for desired statistical power

Test Configuration

Test Type:

Parameters

Effect Size (Cohen's d or r):

Small: 0.2 Medium: 0.5 Large: 0.8

Significance Level (α):

Statistical Power (1-β):

Number of Groups (ANOVA):

Two-tailed test

Help

How to Use

Select your statistical test type from the dropdown
Enter the expected effect size (use Cohen's guidelines if unsure)
Set your desired significance level (typically 0.05)
Set your desired statistical power (typically 0.80)
Click "Calculate Sample Size" to see requirements
Review the power curves and sample size table

Understanding Effect Sizes

t-tests: Cohen's d - Small: 0.2, Medium: 0.5, Large: 0.8
Correlations: r - Small: 0.1, Medium: 0.3, Large: 0.5
ANOVA: Cohen's f - Small: 0.1, Medium: 0.25, Large: 0.4
Proportions: h - Small: 0.2, Medium: 0.5, Large: 0.8

Key Concepts

Statistical Power: Probability of detecting an effect when it exists (avoiding Type II error)
Alpha (α): Probability of Type I error (false positive), usually set to 0.05
Effect Size: Magnitude of the difference or relationship you expect to find
Sample Size: Number of participants needed in each group

Common Mistakes

Using overly optimistic (large) effect sizes leads to underpowered studies
Forgetting that sample size refers to each group, not total participants
Not accounting for expected dropout rates (multiply by 1.2 for 20% dropout)
Using one-tailed tests without strong theoretical justification