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.

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.

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.

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.

Estimates price elasticity of demand from price and volume history or a price test, with the method, confounders, a confidence range and how to use it in pricing. Use before changing prices.

Explains a statistics concept such as a p-value, confidence interval or power, with intuition, a worked example, a simulation and common misreadings. Use to finally get it.

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.

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.

Makes a Fermi estimate by decomposing a quantity, stating assumptions with ranges, cross-checking from another angle and naming the data that would tighten it. Use for sizing when no data exists.

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.

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.

Runs a time-to-event analysis (Kaplan-Meier, Cox) for churn, failure or time-to-hire, handling censoring correctly, with code and a plain reading. Use when the question is how long until.

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.

Writes a reproducible R (tidyverse) analysis script for a described dataset and question, with import, checks, analysis, plots and saved outputs. Use when you need an analysis others can re-run.