power-analysis

Statistical power analysis determines required sample size by specifying effect size, significance level (alpha), desired power (typically 0.8), and test type. Input these parameters into power calculation software or code to compute the minimum sample size that detects your effect with your chosen confidence.

Statistical power is the probability your test detects a true effect if one exists. Higher power reduces the risk of false negatives. Power analysis ensures studies are adequately sized to answer research questions, which is critical for grant proposals, pre-registration, and ethical review.

Yes. Power analysis applies across designs including independent and paired t-tests, one-way ANOVA, chi-square, correlation, and multiple regression. Each test type requires specifying its relevant parameters—effect size, alpha, and sample size—to compute power or determine required sample size.

G*Power provides a graphical interface for power calculations across test types. R and Python offer libraries (like pwr in R) for programmatic power analysis. Both approaches require entering effect size, alpha level, desired power, and test specifications to compute sample size or post-hoc power.

Include sample size justification and sensitivity analyses in grant proposals and ethical review submissions. Power analysis demonstrates study rigor and feasibility. For retrospective analyses, interpret null results using power calculations to assess whether negative findings reflect true absence of effect or insufficient statistical power.

Yes. Retrospective power analysis clarifies whether a null result reflects a genuinely absent effect or inadequate sample size. Sensitivity analyses show what effect sizes your study could reliably detect, supporting conclusions about study limitations and meta-analyses.