CALCULATE WITH CONFIDENCE

Statistics · Sampling & confidence

Statistical Power Analysis Calculator

Solve achieved power, required sample size, or minimum detectable standardized effect for common z-approximation designs.

Statistics · Sampling & confidence

Enter your statistical inputs

Private in-browser calculation · explicit assumptions
Planning balances the effect worth detecting, sample size, false-positive rate, and target power.
  1. 1EnterProvide the known values
  2. 2CalculateResults update automatically
  3. 3VerifyReview the details and units
Try an example

Enter plain numbers without measurement units. Datasets accept commas, spaces, semicolons, or line breaks and are limited to 10,000 values. Results stay in this browser.

Statistics result

—

Enter valid values to see the result.

Your entries are calculated in this browser and are not submitted to 365CALCS.COM.

Feedback

Understand sampling uncertainty

How sample-size and power planning fit together

Statistical Power Analysis. Planning starts with an effect worth detecting, a significance level, a target power, and assumptions about variation or baseline rates.

Method used

Power and planning use normal critical values with design-specific noncentral shifts.

Power is conditional on the effect, variability, alpha, allocation, and model specified before observing results.

What the result cannot establish

Power is a long-run probability under the assumed effect and model. It is not a guarantee of significance, data quality, or practical importance.

Assumption: This planning approximation covers one mean, two equal-sized independent means, and paired means; final consequential designs need method-specific review.

Quick guide

How to use this calculator

  1. Enter the observations, probabilities, model parameters, or summary statistics requested by the visible labels.
  2. Keep every value on the same scale and confirm that the selected sampling relationship, distribution, and tail convention match the question you are investigating.
  3. Read the result together with its assumptions and interpretation. Statistical output summarizes uncertainty under a model; it does not repair biased data or establish causation.

Calculation method

How the statistical power analysis calculator works

Power and planning use normal critical values with design-specific noncentral shifts.

Power is conditional on the effect, variability, alpha, allocation, and model specified before observing results.

Worked example

Statistical Power Analysis example

For a standardized two-group effect of 0.5 and 64 observations per group, approximate two-sided power is about 80%.

Power and planning use normal critical values with design-specific noncentral shifts.

Supported inputs

Precision and limits

Model and design

This planning approximation covers one mean, two equal-sized independent means, and paired means; final consequential designs need method-specific review.

Numerical scope

Inputs use double-precision numerical methods with guarded domains. Datasets accept up to 10,000 finite plain-decimal values. Extremely large parameters or probabilities deep in a numerical tail may require specialist statistical software.

Interpretation

Power is conditional on the effect, variability, alpha, allocation, and model specified before observing results.

Decision boundary

The calculator does not validate how data were collected, diagnose dependence or bias, choose a scientifically meaningful effect, or replace review by a qualified statistician for consequential research, medical, regulatory, safety, or policy decisions.

Privacy

Entered values and calculated results stay in this browser and are not sent to an analytics service.

Continue calculating

Related calculators