What is 80% power in statistics?

80% power means an 80% probability of correctly detecting a true effect if one exists (i.e., a 20% chance of a Type II error). Cohen's convention: power ≥ 0.80 is the minimum acceptable for most research. Clinical trials often require 90–95% power.

How does effect size affect required sample size?

Sample size and effect size have an inverse square relationship. To detect a small effect (d=0.2) at 80% power (α=0.05, two-tailed), you need n≈788 per group; for medium (d=0.5), n≈64; for large (d=0.8), n≈26. Halving the effect size quadruples the required sample size.

Statistical Power Analysis (β) Calculator

Calculate statistical power (1–β) or required sample size for a given effect size (Cohen's d), significance level (α), and sample size. Power = P(reject H₀ | H₂ is true); desired power is typically 0.80 (80%).

Data set — comma or newline separated

Statistical Results — n = 8

Count (n)8

Sum (Σx)40

Min2

Max9

Range7

Q14

Q35.5

IQR1.5

Mean (μ)5

Median4.5

Mode4

Pop. Variance (σ²)4

Pop. Std Dev (σ)2

Sample Variance (s²)4.571428571

Sample Std Dev (s)2.138089935

Skewness0.65625

Excess Kurtosis-0.21875

How to Use

Enter the values for the scenario you are assessing. Calculate statistical power (1–β) or required sample size for a given effect size (Cohen's d), significance level (α), and sample size. Power = P(reject H₀ | H₂ is true); desired power is typically 0.80 (80%). Use the power analysis β result to inform your calculation.

Statistical Power Analysis (β) Calculator

How to Use

Frequently Asked Questions

What is 80% power in statistics?

How does effect size affect required sample size?