MCPower answers two questions about a study before you run it: how much statistical power a given sample size buys, and what sample size reaches a power target. It works by simulating the study many times on a native engine, so the same short workflow covers OLS, logistic, mixed, and factorial models.

Install

pip install mcpower

numpy and pandas are not required. They're accepted as optional input formats (for upload_data and set_correlations) — pip install mcpower[optional] if you want to pass arrays or DataFrames.

A first analysis

Describe the model as a formula, mark which predictors are binary, set the effect sizes you want to be able to detect, and ask for power:

from mcpower import MCPower

# Does a treatment lift satisfaction, controlling for age?
model = MCPower("satisfaction = treatment + age")
model.set_variable_type("treatment=binary")
model.set_effects("treatment=0.5, age=0.3")

result = model.find_power(sample_size=120, target_test="treatment")

Power Analysis — OLS  N=120  sims=1600  α=0.05  target=80%
formula: satisfaction = treatment + age

───────────────────────────────────
Test                 Power   Target
───────────────────────────────────
treatment            77.4%      80%
───────────────────────────────────

That is the whole loop. At n = 120 this design has 77.4% power to detect the treatment effect — just under the conventional 80% target, so you would want a few more participants. The next page asks both questions properly.

The tutorial ladder

Each rung adds one modelling idea and powers it. Climb from the top:

  1. First analysis — the two questions, in full
  2. Interactions — a:b terms
  3. Correlations — correlated predictors
  4. Logistic regression — a binary outcome
  5. Mixed models — grouped / hierarchical data
  6. ANOVA & post-hoc — factors and pairwise contrasts
  7. Multiple testing — corrections across many tests
  8. Custom scenarios — robustness sweeps
  9. Upload data — drive simulation from a CSV or dataframe
  10. Model misspecification — what testing the wrong model costs

New to power analysis? The Concepts pages explain effect sizes, model specification, and what the power number actually means.