Statistical Power & Sample Size Calculator

This calculator determines the **minimum sample size required per group** for an independent samples t-test, or estimates the **statistical power** of a study, based on your inputs.

  • **Alpha ($\alpha$):** The probability of a Type I error (false positive). Typically 0.05.
  • **Power:** The probability of detecting a true effect (avoiding a false negative). Typically 0.80.
  • **Effect Size (Cohen's $d$):** The standardized difference between means. Use 0.2 for small, 0.5 for medium, and 0.8 for large effects.

Input Parameters

Commonly 0.05 (5% chance of Type I error).
Commonly 0.80 (80% chance of detecting a true effect).
**Small:** 0.2, **Medium:** 0.5, **Large:** 0.8. This reflects the magnitude of the difference you expect to find.

Calculation Results

Required Sample Size per Group (n): --
Total Sample Size (N): --

Interpreting Your Results:

  • A **larger effect size** (Cohen's $d$) generally means you need a **smaller sample size** to detect it.
  • **Higher desired power** (e.g., 0.90 instead of 0.80) will require a **larger sample size**.
  • A **smaller alpha level** (e.g., 0.01 instead of 0.05) will require a **larger sample size**.
  • This calculator provides an **estimate**. Real-world studies may need adjustments for factors like dropout rates.
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