Effect Size Power Planner Calculator

Estimate statistical power and adjust sample size to plan reliable experiments.

Quick Facts

Power
Detect Signal
Higher power reduces false negatives
Effect Size
Drives Sample
Smaller effects need bigger samples
Alpha
Controls False Positives
Lower alpha means stricter thresholds
Decision Metric
Estimated Power
Aim for 0.8 or higher

Your Results

Calculated
Estimated Power
-
Power for current inputs
Min Sample
-
Sample per group for target power
Z Threshold
-
Z-score for alpha
Adjusted Effect
-
Effect size after variance

Reliable Power Plan

Your defaults show a solid power level for most studies.

Key Takeaways

  • This tool is built for scenario planning, not one-time guessing.
  • Use real baseline inputs before testing optimization scenarios.
  • Interpret outputs together to make stronger decisions.
  • Recalculate after meaningful context changes.
  • Consistency and execution quality usually beat aggressive one-off plans.

What This Calculator Measures

Estimate statistical power based on effect size, sample size, alpha, and test type to plan experiments.

By combining practical inputs into a structured model, this calculator helps you move from vague estimation to clear planning actions you can execute consistently.

This model estimates power by combining effect size, sample size, and alpha threshold.

How the Calculator Works

Power ≈ Φ(√n × effect − zα)
Z threshold: based on alpha and tails.
Adjusted effect: effect × variance factor.
Min sample: sample for target power.

Worked Example

  • Effect size 0.4 with 80 per group yields moderate power.
  • Lower alpha increases required sample.
  • Variance factor reduces effective effect size.

How to Interpret Your Results

Result BandTypical MeaningRecommended Action
0.9+High power.Strong detection ability.
0.8–0.89Good power.Standard research target.
0.7–0.79Moderate power.Increase sample for confidence.
Below 0.7Low power.Recalibrate design.

How to Use This Well

  1. Enter effect size and sample size.
  2. Select alpha and test type.
  3. Set power target and variance factor.
  4. Review estimated power.
  5. Adjust sample size as needed.

Optimization Playbook

  • Increase sample: biggest power lever.
  • Reduce variance: improve measurement quality.
  • Use realistic effect size: avoid overestimating.
  • Keep alpha consistent: align with reporting standards.

Scenario Planning Playbook

  • Baseline: current effect and sample size.
  • Higher power: increase sample size by 20%.
  • Lower alpha: move to 0.01.
  • Decision rule: target power of 0.8.

Common Mistakes to Avoid

  • Overestimating effect size.
  • Ignoring variance inflation.
  • Using inconsistent alpha levels.
  • Running underpowered studies.

Implementation Checklist

  1. Estimate effect size realistically.
  2. Choose alpha and tails.
  3. Calculate power and adjust samples.
  4. Document assumptions.

Measurement Notes

Treat this calculator as a directional planning instrument. Output quality improves when your inputs are anchored to recent real data instead of one-off assumptions.

Run multiple scenarios, document what changed, and keep the decision tied to trends, not a single result snapshot.

FAQ

What effect size should I use?

Use past studies or pilot data as a reference.

Why does variance matter?

Higher variance reduces the effective effect size.

Can I use one-tailed tests?

Only if direction is justified by your hypothesis.

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