Method document
Achieved power for the Wilcoxon signed-rank test
Calculates the approximate power of the Wilcoxon signed-rank test for paired data, from the probability that the sum of two within-pair differences is positive.
https://statwisely.com/methods/power-signed-rank/
When to use it
- Assess the sensitivity of a before-and-after or matched-pairs comparison when the differences are not plausibly normal.
Not for
- The signed-rank test assumes the differences are symmetric; when they are not, use the sign test.
Notation
- Number of pairs.
- P(Dᵢ + Dⱼ > 0) for two independent differences.
Formulas
Power from the normal rejection region
z₁₋α/tails is the standard-normal critical value.
Parameters
Number of pairs
Values: Integer, at least 2
Complete pairs available for analysis.
Probability of superiority
Values: Between 0 and 1, not 0.5
From pilot data, or for normal differences Φ(Δ√2/σ_D). 0.5 means no effect.
Significance level
Values: Between 0 and 1
The type I error rate for the specified test.
Test direction
Values: 1 or 2
One-sided tests use the upper rejection tail. Two-sided tests split α equally between both tails.
Assumptions
- Pairs are independent and the differences are symmetric about their median. Noether’s formula is a large-sample approximation [1].
- One-sided tests use the upper rejection tail. Two-sided tests split α equally between both tails.
Worked examples
A worked input set
For 90 pairs and p = 0.65 at α = 0.05: |p − ½|√(3 × 90) = 0.15 × 16.4 = 2.46.
Sample size: 90
Open this example in the calculatorValidation
- At the size the signed-rank calculator returns, this power reaches that calculator’s target, and one fewer falls short; it is checked against an independent calculation in R.
How to cite
StatWisely. Achieved power for the Wilcoxon signed-rank test. Method document, engine version 1.0.0, reviewed 2026-10-03. https://statwisely.com/methods/power-signed-rank/
Cite the original references for the formulas your design uses as well.
References
- 1.Noether GE. Sample size determination for some common nonparametric tests. Journal of the American Statistical Association. 1987;82(398):645–647.