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.

Analyses Likert-scale survey data with distribution summaries, diverging bar charts and tests suited to ordinal items or multi-item scales. Use before reporting agreement scores.

Builds a weighted composite index from several indicators, covering direction, normalisation, weighting, aggregation and rank sensitivity checks. Use for scorecards and health scores.

Builds a statistical process control chart with the right chart type, control limits, special-cause rules and an interpretation that separates signal from noise. Use to monitor a process over time.

Calculates and interprets inter-rater reliability, choosing kappa, weighted kappa, Krippendorff's alpha or the right ICC form for the design. Use when checking that coders or judges agree.

Builds a measurement uncertainty budget for a lab or calibration measurement with sources, distributions, sensitivity coefficients, combined and expanded uncertainty, showing all the maths for review.

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.

Computes margins of error and confidence intervals for survey results, including subgroups and gaps between answers, and states what they do not cover. Use before reporting poll numbers.

Designs a choice-based conjoint study with attributes, levels, design, sample size and an analysis plan for preference shares and willingness to pay. Use before pricing decisions.

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.

Evaluates a programme's outcomes with a pre-post or comparison-group design, effect sizes, attrition checks and honest limitations, and drafts funder-ready wording. Use when reporting impact.

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.

Explains what a positive or negative test result means using base rates, sensitivity and specificity, worked through with natural frequencies. Use for medical, screening, fraud or quality tests.

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.

Plans acceptance sampling for incoming goods or production batches with sample size, acceptance number, producer and consumer risks from the operating characteristic curve, and a results record.

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.

Plans and interprets an exploratory or confirmatory factor analysis of survey items, with assumption checks, factor retention, fit indices and reliability. Use when validating a scale.

Builds a Monte Carlo simulation for a decision or forecast with justified input distributions, correlations and code or spreadsheet steps. Use when a single-number estimate hides the risk.

Plans and interprets a multilevel or mixed-effects model for nested or repeated data, with centring, random-effects choices, code, diagnostics and a reporting template. Use when rows are clustered.

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 Python analysis with statsmodels and scipy for a described dataset and question, with data checks, assumption checks and effect sizes. Use when others must re-run it.

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.

Writes a statistical analysis plan before data collection with estimands, models, multiplicity, missing data and sensitivity analyses. Use for trials and pre-registrations.