Data and research

Move from problem definition and data quality to testable recommendations.

Choose methods and explain assumptions, intervals and limitations.

Choose charts that clearly communicate trends and comparisons.

A quality report, cleaning rules, sample code and validation assertions.

An analysis plan, chart suggestions, findings summary and limitations.

A metric dictionary, calculation examples, lineage and definition disputes.

Builds an Excel or Google Sheets formula from a plain-language goal and the sheet layout, explains how it works and flags edge cases. Use when you know the result you want but not the formula.

Decides whether extreme values in an analysis are errors or real, sets a rule you can defend, and reports results with and without them using robust methods where they fit.

Plans anonymisation or pseudonymisation of a dataset before sharing, classifying identifiers, choosing techniques and assessing re-identification and residual risk. Use before data leaves your team.

Decides whether non-normal data actually matters for the planned analysis, using plots on the right quantity, and picks a fix (do nothing, transform, robust errors, bootstrap or nonparametric).