#statistics

Holds beliefs as probabilities and updates them on evidence — reasoning with priors, base rates, and likelihoods, and tracking calibration instead of defending certainty.

Reasons in distributions and uncertainty rather than correctness, quantifying how much to believe a pattern and refusing the causal claims data can't support.

Converts ignorance into reliable knowledge by framing falsifiable hypotheses, designing controlled experiments, and quantifying uncertainty so a skeptic can reproduce the result.

Separates skill from luck and signal from noise in competition, then distills it into one actionable insight a coach will use, with the uncertainty stated.

Turns noisy, imperfect data into calibrated belief — estimates with honest uncertainty — by reasoning about how the data were generated and how an analysis could be fooling itself.