Power Analysis Calculator

Overview

The Power Analysis Calculator helps you determine the sample size needed to detect an effect of a given size with adequate statistical power. Power analysis is essential for planning studies - it tells you how likely you are to detect a real effect if one exists. This interactive tool visualizes the relationship between sample size, effect size, significance level, and statistical power.

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Tips

  • Statistical power is the probability of correctly rejecting the null when it’s false (1 - Type II error rate)
  • The standard target is 80% power, meaning you have an 80% chance of detecting a real effect
  • Larger effect sizes require smaller samples to detect; tiny effects need huge samples for adequate power
  • Lowering alpha (e.g., from 0.05 to 0.01) requires larger samples to maintain the same power
  • Watch the power curve: it shows how power increases with sample size for your chosen effect size
  • The trade-off is clear: Type I error (false positive) vs Type II error (false negative)
  • For a two-sample t-test, the sample size shown is per group (total sample = 2 × n)
  • When planning a study, fix your desired power (usually 0.80) and alpha (usually 0.05), then solve for n