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Attribution Model Statistical Power Calculator

This calculator estimates approximate statistical power for detecting an increase between two independent proportions with equal group sizes for attribution model using a transparent statistical or operational model. It is designed for analytics teams that need a quick numerical check before interpreting adoption, segmentation, or model-performance data. The result converts a small set of measurable inputs into a consistent metric that can be compared across periods, cohorts, or implementation scenarios.

It is intended for early experiment planning when a team wants to see how sample size, baseline rate, detectable change, and significance threshold interact. Treat the output as a planning estimate rather than a substitute for a full experiment design or production capacity study. Data quality, sampling method, workload mix, and system architecture can materially change real-world results, so the calculator makes its assumptions visible and keeps the inputs editable.

Calculator inputs

%
pp
Result
—
Calculated result
baseOut—
altOut—
zOut—
powerOut—

1. Enter observations per group
Use the planned or available number of independent observations in each of two groups.

2. Set the baseline proportion
Enter the expected rate for the reference group.

3. Set the detectable difference
Enter the absolute percentage-point increase you want the test to be able to detect.

4. Choose significance
Select the two-sided alpha level used for the rejection threshold.

5. Review approximate power
Higher power means a greater modeled chance of detecting the specified difference when it is real.

Alternative proportion: p₂ = p₁ + d

Standard error under the alternative: SE = √(p₁(1−p₁)/n + p₂(1−p₂)/n)

Signal: z = d / SE

Approximate two-sided power: Power ≈ Φ(z − zα/2) + Φ(−z − zα/2)

This is a normal-approximation planning model for two independent proportions with equal group sizes. Exact test power can differ, especially for small samples or extreme rates.

What the result means

The main result estimates the probability that a two-sided test will reject the null hypothesis when the entered absolute increase is the true effect.

Power is model-dependent. Dependence between observations, unequal groups, multiple testing, or different test statistics require a tailored analysis.

Given: 500 observations per group, a 30% baseline rate, a 5 percentage-point increase, and α = 0.05.

Calculation: p₂ = 0.35. SE = √(0.30×0.70/500 + 0.35×0.65/500) = 0.02958. Signal z = 0.05 / 0.02958 = 1.690. Approximate two-sided power ≈ Φ(1.690 − 1.960) + Φ(−1.690 − 1.960) ≈ 39.4%.

Result: Approximate power is about 39%.

Interpretation: At this sample size, the specified 5-point increase would often be missed at the 5% two-sided significance level.

What does statistical power represent?

It is the probability of detecting the specified effect under the assumptions of the test when that effect is truly present.

Is detectable difference relative or absolute?

This calculator uses an absolute percentage-point increase. A baseline of 30% with a 5-point difference implies an alternative rate of 35%.

Why does a stricter significance level reduce power?

A smaller alpha requires stronger evidence to reject the null hypothesis, which makes detection harder at the same sample size and effect.

Can I use unequal group sizes?

This implementation assumes equal observations per group. Unequal allocation changes the standard error and should be analyzed separately.

Is 80% power always required?

Eighty percent is a common planning convention, not a universal rule. The appropriate target depends on decision costs, feasibility, and the consequences of missed effects.