Why is 80% power the standard?

80% is a long-standing convention that balances the cost of a larger sample against the risk of missing a real effect. 90% is stronger but needs more participants. Below about 80%, a non-significant result is hard to interpret because you can't tell a true null from an underpowered study.

Should I compute power after my study?

Not from the observed effect — 'observed' or post-hoc power is circular and uninformative. Base power on a meaningful target effect size taken from prior work, which is what a proper a priori power analysis does.

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What is statistical power?

Q: What is statistical power?

Statistical power is the probability that your study will detect a real effect of a given size if it truly exists. A power of 0.80 means an 80% chance of finding the effect and a 20% chance of missing it (a Type II error).

Q: What is a power analysis?

A power analysis links four quantities — power, sample size, effect size, and significance level (alpha). Fix any three and you can solve for the fourth. Before a study you usually fix power, effect size, and alpha to solve for the sample size you need to recruit.

By Dr. Rafiq Muhammad, MD, PhD · Updated June 2026

Statistical power is the probability that your study will detect a real effect of a given size, if that effect truly exists. Power of 0.80 means an 80% chance of finding the effect — and a 20% chance of missing it (a Type II error). Power is the single best defence against the most demoralising research outcome: a true effect that your study was simply too small to see.

The four quantities that interlock

Power doesn’t stand alone. It’s locked together with three others, and fixing any three determines the fourth:

Power — your chance of detecting the effect (aim for ≥ 0.80).
Sample size — how many participants you recruit.
Effect size — how big the effect you want to detect is.
Significance level (alpha) — your false-positive risk, usually 0.05.

Bigger samples, larger effects, and a more lenient alpha all raise power. That’s why a tiny study chasing a small effect is almost guaranteed to be underpowered.

Why 80% — and why you decide it before you start

80% is the conventional minimum (90% is stronger). The crucial point is when you compute it: a proper a priori power analysis happens before data collection — you fix power, your target effect size, and alpha, then solve for the sample size to recruit. Computing power after the fact from your observed effect (“post-hoc” or “observed” power) is circular and tells you nothing useful — reviewers will flag it.

How to run a power analysis

You need a realistic target effect size — ideally from a prior study or a pilot, not a hopeful guess (over-estimating the effect is the most common route to an underpowered study). Then plug power, effect, and alpha into a calculator to get your sample size; or enter a sample size you can realistically recruit to see the power it buys you.

Do it now: the free Sample Size Calculator turns your target effect, power, and confidence into a number to recruit — and the Power Calculator tells you the power a given sample size achieves. (Always inflate for expected dropout.)

Get the free Statistics toolkit

Power and sample-size worked examples, effect-size tables, and a planning checklist from the Statistical Test Selection Workbook. We’ll email you the download link.