Estimate the power of a two-group comparison for detecting a change in a feature-store quality rate. Enter baseline and target proportions plus the sample size per group to see approximate detection power, absolute effect, and relative improvement.
The calculation uses equal group sizes and a two-sided normal approximation for two independent proportions.
Use the main result together with the supporting bounds, counts, or capacity figures. The estimate is only as reliable as the input definitions, sampling process, and operating assumptions.
This is a normal-approximation, equal-group, two-sided test. Dependence between records, multiple testing, and sequential monitoring can change actual power.
Comparing 8% with 5% using 900 observations per group produces a 3.0-percentage-point effect. The calculator converts that effect and its standard errors into approximate power.
Power is the approximate probability that the specified two-sided test detects the entered difference when that difference is real.
Small differences are harder to separate from random sampling variation, so more observations are required.
It is the number of observations per group. The displayed total is twice that value for two equal groups.
No. It describes a long-run detection probability under the assumptions, not a guarantee for one experiment.
Not directly. Paired or repeated measurements require a method that accounts for within-item correlation.
| Input | Meaning |
|---|---|
| Baseline rate | Expected control proportion |
| Target rate | Smallest effect of interest |
| Sample per group | Independent observations in each arm |