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Calculating sample size or detectable effect given desired power and significance.
DESCRIPTION
Power analysis quantifies the probability of detecting a true effect given assumptions about variance, effect size, and sample size at a chosen alpha. Prospective power guides experiment design, while post-hoc power is generally discouraged. It helps set minimum detectable effect, budget test duration, and avoid underpowered studies. Inputs include baseline rates, variance, allocation, and test type. Variance reduction and sequential designs can reduce required sample sizes with proper controls.
RELATED TERMS
Statistical Power Effect Size Sample Size Significance Level Type I Error Type II Error Minimum Detectable Effect Hypothesis Testing Variance Reduction
COMPETING TERMS
Bayesian Design Analysis Sequential Testing
KNOWLEDGE AREAS
Mathematics Data Science Testing & Validation
TARGET AUDIENCE
Data Scientist Data Analyst BI Specialist AI Product Manager Product Owner
EXTERNAL SOURCES
- Wikipedia - Statistical power
- UCLA IDRE - Introductory guide to power analysis
- Penn State Eberly - Power and sample size concepts
- NIST - NIST e-Handbook on power and sample size
TOP QUESTIONS
Why is Power Analysis essential before running an experiment?
It ensures sufficient sample size to detect meaningful effects, preventing wasted time and misleading null results. It aligns expectations on duration and impact.
Category: Relevancy
What inputs are needed for Power Analysis?
Effect size, variance or baseline rate, desired alpha and power, allocation ratio, and test type. These determine sample size or detectable effect.
Category: Requirements
How does Minimum Detectable Effect relate to Power Analysis?
MDE is the smallest effect you aim to detect with specified power and alpha. Lower MDE needs larger samples or variance reduction.
Category: Concept
What are common mistakes in Power Analysis?
Underestimating variance, ignoring multiple comparisons, and misusing post-hoc power. Review assumptions and validate with pilot data when possible.
Category: Mistakes
Can sequential designs reduce required sample size?
Yes, group sequential and alpha-spending approaches can stop early for efficacy or futility, maintaining error control.
Category: Techniques
How do variance reduction techniques affect power?
Methods like CUPED or covariate adjustment reduce noise, effectively increasing power for a fixed sample size. Ensure assumptions hold.
Category: Techniques
Should you use post-hoc Power Analysis?
Generally no; it often mirrors p-values and can mislead. Focus on confidence intervals and pre-study design power.
Category: Opinion
How to communicate Power Analysis to stakeholders?
Present assumptions, MDE, expected duration, and risks. Provide sensitivity analyses for alternative scenarios to build confidence.
Category: Communication
Is Bayesian Design Analysis a replacement for Power Analysis?
It’s an alternative framework using priors and decision criteria. Choose based on organizational preference and problem context.
Category: Alternatives