How to use this calculator
- Enter the expected baseline rate.
- Enter the smallest alternative rate worth detecting.
- Enter completed observations per group.
- Select the two-sided significance level and review approximate power.
#3170 · AI & Technology Tool
Dashboard Adoption Statistical Power Calculator
Estimate whether a two-group dashboard adoption test is large enough to detect a specified change. Enter baseline adoption, the alternative adoption rate worth detecting, completed users per group, and a two-sided significance level. The calculator reports approximate power, absolute and relative lift, and total sample. It assumes independent users and equal groups; repeated user observations, team-level assignment, unequal allocation, or sequential testing need adjusted methods.
Calculator
Two-group designFormula
Power ≈ Φ((|p₂−p₁| − zα/2·SE₀) ÷ SE₁) + lower-tail term
The approximation uses equal independent group sizes, a pooled null standard error, and an alternative standard error.
What the result means
Higher power means a better chance of detecting the specified rate difference if it is real. It is not the probability that the alternative hypothesis is true.
This planning approximation is not suitable for clustered assignment, repeated observations, sequential monitoring, or very sparse expected counts.
Example calculation
With dashboard adoption of 30% versus 36%, 800 users per group, and a 5% two-sided significance level, approximate power is 72.4%, absolute lift is +6.00 points, and total sample is 1,600.
Tips for better results
- Choose the smallest effect that would change a decision.
- Base the baseline rate on relevant prior data.
- Plan for completed, usable observations.
- Account for clustering before launch.
- Fix the significance and analysis plan in advance.
Frequently asked questions
What does statistical power mean here?
It is the approximate probability that the planned two-sided test detects the entered difference when that alternative is true.
Why must the baseline and alternative rates differ?
Power is defined for a nonzero effect. If the rates are identical, there is no specified difference for the test to detect.
Does a lower significance level reduce power?
Usually yes for the same sample and effect because stronger evidence is required before rejecting the null hypothesis.
Can I use unequal group sizes?
This calculator assumes equal completed samples. Unequal allocation needs a formula that uses each group size separately.
Does this account for clustered or repeated observations?
No. Dependence reduces effective information and should be handled with a design effect or a model suited to the assignment and outcome structure.
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Power design inputs
| Input | Purpose |
|---|---|
| p₁ | Baseline proportion |
| p₂ | Alternative proportion |
| n | Observations per group |
| α | Two-sided significance level |