Power Analysis Loop

A power-analysis-and-preregister loop for a two-arm comparison. The artifact is the study's statistical plan; the feedback signal is two parts — statistical power (estimated by Monte-Carlo simulation of the planned test) and a count of validity flaws. Each iteration simulates power, solves for the sample size that reaches the target, audits the design for flaws, and revises — until power clears the target and the flaw list is empty. The deliverable is a sample-size justification plus a preregistration that pins the hypothesis, primary outcome, analysis, sample size, and stopping rule before any data is seen.

Scope & limitations

This loop does exactly three things, in a loop: (1) computes power and required sample size for a two-group comparison by simulation, (2) runs a fixed validity checklist over the design, and (3) writes a preregistration. The vendored power model (tools/power_sim.py) covers two-sample mean (continuous outcome) and two-proportion (binary outcome) tests only.