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Power analysis
Sample size for t-test, correlation, chi-square
Cohen’s rule of thumb: d 0.2 / 0.5 / 0.8 · r 0.1 / 0.3 / 0.5 · w 0.1 / 0.3 / 0.5 (small / medium / large). Use values from similar studies if you have them.
You need 64 people per group, 128 in total.
What does it mean?
Power is the chance of detecting an effect of this size if it really exists. 80 % is the usual minimum. The calculation is a proven normal approximation.
Learn more
- Sample sizeFor a survey with 95% confidence and a ±5% margin of error you need about 385 responses. For group comparisons, a power analysis sets the size.
- Writing a hypothesisA hypothesis is a testable prediction about the relationship between variables. Write a null (H₀) and an alternative hypothesis (H₁) for each test.
- Effect sizeAn effect size tells you how large a difference or relationship is, independent of sample size. Cohen’s d of 0.2, 0.5 and 0.8 is small, medium, large.
- Type I and Type II errorsA Type I error is a false positive (rejecting a true null hypothesis, risk α); a Type II error is a false negative (missing a real effect, risk β).
- t-testA t-test checks whether two means differ significantly: two independent groups, two paired measurements, or one mean against a fixed value.
- ANOVAAnalysis of variance (ANOVA) tests whether three or more group means differ. A significant F-test is followed by post-hoc tests such as Tukey HSD.
