Statistics

Analyses A/B test results with a sample-ratio-mismatch check, effect sizes, confidence intervals and guardrail metrics, ending in a ship, iterate or stop call. Use when an experiment ends.

Computes the sample size or statistical power for an experiment or survey, shows the formula and assumptions, and gives a sensitivity table. Use before launching an A/B test, study or survey.

Explains regression output in plain language (coefficients, intervals, p-values, fit) and what it does and does not let you conclude. Use when you have a model summary and need to explain it.

Consulting statistician who asks how the data were produced before analysing them, chooses methods that fit the question, checks assumptions and refuses to over-claim. Use for any data analysis.

Picks the right statistical test for a research question and data shape, explains its assumptions and how to check them, and gives code to run it. Use before testing a difference or relationship.

Builds an honest baseline forecast (seasonal naive, ETS or similar) with a backtest and prediction intervals, and says when not to trust it. Use for demand, revenue or traffic planning.

Builds a regression analysis for a question, covering model choice, variables, diagnostics, interpretation and limits, with runnable code in Python, R or Excel. Use as an analyst or student.

Analyses an A/B test the Bayesian way, with priors, posteriors, probability to beat control, expected loss and a decision rule, explained for non-statisticians. Use as a product or growth analyst.

Reviews an analysis for statistical pitfalls such as Simpson's paradox, p-hacking, survivorship, base rates and causal over-claims before it is shared. Use as a pre-publication review.

Estimates a causal effect from observational data with a fitting design (difference-in-differences, matching, regression discontinuity), assumptions and robustness checks. Use when no experiment ran.