Is this an a priori power analysis?

Yes. It solves for the sample size required before data collection, given effect size, α, and target power (commonly 0.80).

Which effect-size metric should I enter?

Each test uses its standard metric — Cohen's d for t-tests, f for ANOVA, r for correlation, w for chi-square, h for proportions. The widget shows presets for small/medium/large.

Can I embed it without the StatMate badge?

No — the attribution link is required and is what keeps the widget free.

Embeddable Widget

Sample Size & Power Calculator — Free Embed

Help researchers plan studies right on your page. Visitors choose a test, set the expected effect size, significance level (α), and desired power (1−β), and the widget returns the minimum sample size needed — plus a power curve to see how n trades off against detectable effect.

Copy & paste this embed code

Paste it into any HTML page, blog post, Notion embed, or website builder. No account needed.

<iframe src="https://statmate.org/embed/sample-size" width="100%" height="1240" style="border:1px solid #e5e7eb;border-radius:12px;max-width:760px" loading="lazy" title="Sample Size Calculator by StatMate"></iframe>

Live preview

What the Sample Size & Power calculator computes

Required sample size for two-sample, paired, and one-sample t-tests
Sample size for ANOVA, correlation, chi-square (2×2), and proportions
Inputs for effect size (Cohen's d, f, r, w, h), α, and power
Small/medium/large effect-size presets per test
Power curve visualization and APA-style summary

How to embed it

Copy the embed code above.
Paste it into your page's HTML where you want the calculator to appear.
Adjust width and height if needed — the widget is responsive.
Publish. The calculator runs entirely in your visitor's browser.

Who uses it

Grant and IRB pages that require an a priori power justification
Methods tutorials explaining how to size a study
Research labs standardizing study-planning across students

Frequently asked questions

Is this an a priori power analysis?: Yes. It solves for the sample size required before data collection, given effect size, α, and target power (commonly 0.80).
Which effect-size metric should I enter?: Each test uses its standard metric — Cohen's d for t-tests, f for ANOVA, r for correlation, w for chi-square, h for proportions. The widget shows presets for small/medium/large.
Can I embed it without the StatMate badge?: No — the attribution link is required and is what keeps the widget free.

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Open the full Sample Size & Power calculator →

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