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)
Get properly formatted citations for academic work •20k+ calculations in the past 30 days
StatsCalculators Team. (2026). Sample Size & Power Analysis Calculator. StatsCalculators. Retrieved February 15, 2026 from https://statscalculators.com/calculators/hypothesis-testing/sample-size-and-power-analysis-calculator
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.
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)
Get properly formatted citations for academic work •20k+ calculations in the past 30 days
StatsCalculators Team. (2026). Sample Size & Power Analysis Calculator. StatsCalculators. Retrieved February 15, 2026 from https://statscalculators.com/calculators/hypothesis-testing/sample-size-and-power-analysis-calculator
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.
