This sample size calculator helps you determine the optimal sample size needed for your statistical tests. It provides comprehensive power analysis with visual charts showing the relationship between sample size, effect size, and statistical power. Whether you're comparing means (paired/unpaired), proportions, multiple groups (ANOVA), categorical associations (chi-square), or building predictive models (multiple regression), this calculator will help ensure your research has adequate statistical power to detect meaningful effects.
If you are looking to calculate the sample size based on a desired margin of error for confidence intervals, try our Margin of Error Sample Size Calculator.
Range: 0.1 to 2 (standard deviations)
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StatsCalculators Team. (2026). Sample Size & Power Analysis Calculator. StatsCalculators. Retrieved May 18, 2026 from https://statscalculators.com/calculators/hypothesis-testing/sample-size-and-power-analysis-calculator
You are planning a new study.
Choose a test, alpha, target power, and the effect size to detect.
You already know how many observations you can collect.
Enter sample size and effect size to check whether the study is adequately powered.
Your sample size is fixed by budget, time, or available data.
Enter sample size and target power to learn the smallest effect your design can detect.
| If your study design is... | Choose this test type | Input tip |
|---|---|---|
| Two independent groups, continuous outcome | Mean Difference (Independent t-test) | Use Cohen's d, such as mean difference divided by pooled SD. |
| Same people measured twice or matched pairs | Mean Difference (Paired t-test) | Enter the expected within-pair correlation when known. |
| Two conversion rates, response rates, or proportions | Proportion Test | Enter the baseline rate and the expected absolute difference. |
| Three or more independent group means | ANOVA | Use Cohen's f and the number of groups. |
| Categorical counts or contingency tables | Chi-Square Test | Use Cohen's w and the appropriate degrees of freedom. |
| Continuous outcome with multiple predictors | Multiple Regression | Use Cohen's f-squared and the number of predictors. |
Use the full tutorial when you still need to choose the test, estimate an effect size, or adjust for practical constraints.
Statistical power is the probability that your study will correctly detect an effect when there is one. Failing to do so results in a Type II error.
A power of 0.8 (or 80%) is typically considered adequate, indicating there is a 20% chance of overlooking a real effect.
Sample size calculation is a crucial step in research design and hypothesis testing. It helps you:
Warning: Conducting a study with inadequate sample size can lead to:
You're testing a new button design and want to detect a 2% increase in conversion rate (from 10% to 12%).
Without proper sample size calculation:
For this example, we need:
Input these values into the calculator and it will give you 3841 samples per group.
While traditional sample size calculation is crucial, modern A/B testing platforms often use sequential testing approaches:
Whether using traditional fixed-sample approaches or modern sequential methods, proper planning of sample size and monitoring procedures is essential for valid and reliable results.
