Master Statistical Power Analysis with AI

Statistical power analysis is the cornerstone of robust research design. Whether you're planning a clinical trial, designing an A/B test, or conducting academic research, understanding power helps you make informed decisions about sample sizes and detect meaningful effects.

But here's the thing—traditional power analysis often involves complex formulas, multiple software packages, and hours of calculations. What if you could perform comprehensive power analysis using natural language, get instant results, and explore different scenarios with AI assistance?

The Four Pillars of Power Analysis

Real-World Power Analysis Examples

Example 1: Clinical Trial Sample Size

A pharmaceutical company wants to test whether a new blood pressure medication is more effective than the current standard. They need to detect a 5 mmHg difference in systolic blood pressure reduction.

Using historical data, they know the standard deviation is approximately 15 mmHg. With α = 0.05, power = 0.80, and a two-tailed test, they need 143 participants per group (286 total).

Example 2: A/B Test for Conversion Rate

An e-commerce platform currently has a 3% conversion rate and wants to detect a 20% relative improvement (from 3% to 3.6%). For a power of 0.80 and α = 0.05, they need approximately 8,800 visitors per variant.

Example 3: Academic Research - ANOVA Design

A psychology researcher wants to compare four different therapy approaches. They expect a medium effect size (f = 0.25) and want 90% power. The analysis shows they need 45 participants per group (180 total).

How to Perform Power Analysis in Sourcetable

When You Need Power Analysis

Beyond Basic Power Analysis

Multiple Comparisons & Family-Wise Error Rate

When you're testing multiple hypotheses, your effective alpha level changes. The Bonferroni correction is conservative but simple: divide α by the number of comparisons. For more sophisticated approaches, consider the False Discovery Rate (FDR) method.

Unequal Group Sizes

Real-world studies often have unequal groups. The harmonic mean formula adjusts for this: n_harmonic = 2 × (n1 × n2) / (n1 + n2). This gives you the 'effective' sample size for power calculations.

Non-Normal Data & Non-Parametric Tests

When your data doesn't follow normal distributions, non-parametric tests like Mann-Whitney U or Kruskal-Wallis may be more appropriate. These typically require 15-20% larger sample sizes to achieve equivalent power.

Repeated Measures & Longitudinal Studies

Studies with repeated measurements benefit from reduced within-subject variability. The correlation between repeated measures can dramatically reduce required sample sizes—sometimes by 50% or more.

Avoiding Power Analysis Pitfalls

Mistake 1: Using Inappropriate Effect Sizes

Cohen's conventions (small = 0.2, medium = 0.5, large = 0.8) are guidelines, not rules. Always base effect sizes on previous research, pilot data, or clinical significance thresholds.

Mistake 2: Ignoring Practical Constraints

Your power analysis might suggest 500 participants, but you only have access to 100. Consider increasing your effect size threshold or using more sensitive measures rather than proceeding with underpowered studies.

Mistake 3: Post-Hoc Power Analysis

Calculating power after seeing your results is largely meaningless. If you didn't find significance, the post-hoc power will be low by definition. Focus on confidence intervals and effect size estimation instead.

Mistake 4: Forgetting About Attrition

Longitudinal studies and clinical trials often lose participants. Inflate your initial sample size by the expected dropout rate: n_adjusted = n_calculated / (1 - dropout_rate).