Sample size calculator
Work out how many participants you need — to compare two groups with adequate statistical power, or to estimate a proportion or mean to a target margin of error. Free, no sign-up; everything runs in your browser.
Uses standard normal-approximation formulas (Cohen, 1988; survey CI). For comparison tests, add ~10–15% for expected dropout, and consider exact / simulation-based power for small samples or complex designs. Everything runs in your browser.
Designing the study?
Plan the sample here, run the review with Folio's meta-analysis and PRISMA tools, then write it up with citation-checked drafting.
Why plan your sample size?
An underpowered study risks missing a real effect; an oversized one wastes resources and can raise ethical concerns. A sample-size calculation — done before you collect data — ties your target effect size, significance level, and desired power to the number of participants you actually need, and it's expected in pre-registrations, grant applications, and ethics submissions.
This calculator pairs with Folio's free meta-analysis and PRISMA tools — plan the study here, then analyze and write it up in one workspace.
Frequently asked
Is this sample size calculator free?
Yes — completely free, with no sign-up. All calculations run in your browser; nothing is uploaded.
What does statistical power mean?
Power (1 − β) is the probability of detecting an effect that truly exists. 80% is the conventional minimum; 90% is common when a missed effect is costly. Higher power requires a larger sample.
What is Cohen’s d?
A standardized effect size for a difference in means: the difference divided by the standard deviation. Conventional benchmarks are 0.2 (small), 0.5 (medium), and 0.8 (large) — but use an estimate from prior work or a pilot when you can.
How is the survey sample size calculated?
For estimating a proportion, it uses n = z²·p·(1−p) / margin², with an optional finite-population correction when you know the population size. With p unknown, 0.5 gives the most conservative (largest) sample.
How accurate are the numbers?
These are standard normal-approximation formulas used by most calculators and are excellent for planning. For small samples, sequential designs, clustering, or complex models, confirm with exact or simulation-based power (e.g. G*Power or a stats package). Add a margin for expected dropout.