Biostatistics and epidemiology
P-values, type I and II errors and statistical power
What a p-value does and doesn't mean, type I and type II errors, what sets power and sample size (with worked calculations), multiplicity, subgroups and interim analyses.
Tier 1Medical sciences concept
Written with AI assistance and fact-checked against the sources below. For exam study, not clinical decisions. How notes are made
At a glance
- A trial starts by assuming the null hypothesis (no difference). The p-value is the probability of data at least as extreme as those observed if the null were true.
- Type I error (alpha): a false positive. Type II error (beta): a false negative. Power = 1 − beta, the chance of detecting the target effect if it exists.
- Sample size depends on alpha, power, the effect worth detecting and the variability of the outcome. Halving the target effect quadruples the sample.
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Sources
- The ASA statement on p-values: context, process, and purposeThe American Statistician (Wasserstein and Lazar, 2016;70:129-33), 2016
- ICH E9: Statistical principles for clinical trials, and E9(R1) addendum on estimands and sensitivity analysisInternational Council for Harmonisation (1998; addendum 2019), R1, 2019
- Users' Guides to the Medical Literature: A Manual for Evidence-Based Clinical Practice (3rd ed.)JAMA Evidence / McGraw-Hill (Guyatt et al., 2015), 2015
- Randomised trial of intravenous streptokinase, oral aspirin, both, or neither among 17,187 cases of suspected acute myocardial infarction: ISIS-2Lancet (ISIS-2 Collaborative Group, 1988;332:349-60), 1988
- Statistics notes: Absence of evidence is not evidence of absenceBMJ (Altman and Bland, 1995;311:485), 1995
- Last updated
- 14 November 2026
What changed
- 14 November 2026 Personal emails, About story and Australian-style copy