Statistical Power Calculator
Online statistical power calculator for A/B tests and experiments. Estimate power from effect size, sample size, standard deviation or proportions. Includes formulas, interpretation, and sample size suggestion.
Full original guide (expanded)
Statistical Power Calculator
Estimate the power of a two-group experiment or A/B test from effect size, sample size, and alpha. Supports tests on means and on proportions.
1. Choose test type
2A. Inputs for means
Example: control mean = 10, treatment mean = 12 → effect size = 2. SD is the common standard deviation.
3. Test settings
4. Results
Estimated power
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Effect size (absolute)
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Indicative n per group for 80% power
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What is statistical power?
Statistical power is the probability that your test will detect an effect if the effect is really there. Low power → high chance of false negatives.
Key determinants of power
- Effect size: bigger differences are easier to detect.
- Sample size: more data → narrower standard error → higher power.
- Alpha: a higher alpha (e.g. 0.1) makes it easier to reject H0, increasing power.
- Variability: lower standard deviation → higher power.
Formula idea (z-approximation)
For a two-sample test on means (equal n), the test statistic roughly follows
z = (μ₂ − μ₁) / (σ √(2/n))We compare this to the critical value for the chosen α and tails, then compute the corresponding power as the probability the test statistic falls in the rejection region.
Formula (LaTeX) + variables + units
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z = (μ₂ − μ₁) / (σ √(2/n))
- No variables provided in audit spec.
- NIST — Weights and measures — nist.gov · Accessed 2026-01-19
https://www.nist.gov/pml/weights-and-measures - FTC — Consumer advice — consumer.ftc.gov · Accessed 2026-01-19
https://consumer.ftc.gov/
Last code update: 2026-01-19
- Initial audit spec draft generated from HTML extraction (review required).
- Verify formulas match the calculator engine and convert any text-only formulas to LaTeX.
- Confirm sources are authoritative and relevant to the calculator methodology.