| name | evidence-audited-analysis |
| description | Data-skepticism discipline for quantitative work. Use whenever reading data and producing a conclusion: analytics questions, metrics reports, spreadsheet analysis, SQL queries, dashboards, A/B results, forecasts, or any claim of the form "the data shows." Trigger before presenting any number or trend to a user. |
Evidence-Audited Analysis
The most common analytical failure is not a wrong model. It is a confident, well-written narrative built on numbers nobody interrogated. Audit the evidence before you analyze it, and state what it cannot show alongside what it can.
Interrogate the data first
Before any analysis, profile what you were given and report anomalies:
- Shape: row counts, date coverage, obvious gaps. An analysis of "last quarter" on a table that only starts mid-quarter is wrong before it begins.
- Quality: nulls, duplicates, impossible values (negative durations, future timestamps), unit surprises (cents vs dollars, UTC vs local).
- Meaning: for every field you lean on, answer in writing: what does this number actually measure, and who put it there? A "deal created" count measures logging behavior, not demand. A "page view" count includes bots unless someone excluded them. If a human process feeds the field, the field inherits that process's habits, and you must ask about them before treating the field as ground truth.
If a profiling step surfaces something odd, resolve it or disclose it. Never silently analyze around it.
Audit your own numbers
- Reproduce every headline number a second, independent way before reporting it: a different query, a different aggregation path, or a manual spot-check of raw rows. If two routes disagree, that is a finding about the data, and it comes before any other finding.
- Baseline before model. Report the naive answer (last period's value, the overall average, the simplest split) before anything sophisticated. If the sophisticated answer does not beat the baseline meaningfully, say so; the baseline is the finding.
- Small samples get small claims. State the n behind every rate or comparison. "Conversion doubled" on 4 versus 2 events is noise wearing a trend's clothes. When the sample cannot support the conclusion, the honest deliverable is "this data cannot answer that yet," with what would be needed.
Gate the language
- Correlation stays correlational until you can name a mechanism and have considered at least one alternative explanation (seasonality, mix shift, a tracking change, a policy change). Only then may "because" appear.
- Every deliverable includes a "what this cannot tell you" section: the questions the data looks like it answers but does not, the populations it excludes, the biases in how it was collected.
- Uncertainty is part of the number. Ranges, caveats, and confidence live next to the figure they qualify, not in a footnote after the reader has already anchored.
Why this matters
Downstream decisions inherit upstream credulity. A skipped "what does this field actually measure" step does not fail loudly; it produces a plausible chart, a confident recommendation, and a wrong decision weeks later. The discipline is cheap: minutes of profiling and one independent recomputation, purchased against conclusions that are expensive precisely because they are believed.
