Data Analysis

Data Analysis

Derive reviewable conclusions from the user's data. Use the available source and computation tools; this Skill does not prescribe a format, language, library, or artifact. Respect project metric definitions and business rules. Preserve inputs unless transformation was requested, and match effort to the decision's risk.

Apply three gates before analysis:

• Precision gate: execute precision-preserving arithmetic before reporting an exact monetary/ledger reconciliation or another result where ordinary numeric arithmetic can lose meaningful digits. Reading values, profiling them, or writing out arithmetic is not verification.
• Method gate: report no statistical interval, p-value, model coefficient, forecast, or probability unless an applicable method was executed and its assumptions are supported by the evidence. Calling a number rough or approximate does not supply a derivation.
• Confidence gate: return no 0-100 or other numerical confidence unless the source defines the score or an applicable method estimates that quantity. Otherwise say it is not estimable from the supplied evidence. Never relabel an unsupported number as evidence strength, decision confidence, reliability, or confidence in the opposite conclusion. When the user requests numerical confidence, execute the bundled uncertainty-gate after classifying the claim and evidence; its rejection of a generic score is binding.

Evidence boundary

Every reported number needs a derivation, a unit, and a denominator where applicable. Apply this to the final recommendation, not just the calculations.

• Units: retain source units unless a conversion is needed and supported. If the currency is unspecified, say so and keep the supplied monetary unit; do not guess a currency or silently scale the values. Label totals versus per-unit values and percentage points versus relative percentages.
• Confidence: identify the requested quantity and whether the design/data can estimate it. A user's request for a number does not create evidence, and not estimable is not zero probability. A defined business scoring rule may be computed, but is not a probability.
• Causality: a descriptive comparison, of any sign, establishes neither a causal effect nor its absence without a credible identifying design. Report the observed result separately from what remains unidentified.

Analytical loop

1. Frame the claim. Establish the population, entity grain, period, metric, denominator, units, comparison, and deliverable. Distinguish description, prediction, and causal inference. Resolve ambiguities that could change the answer; otherwise state a reasonable assumption.
2. Establish the evidence. Inspect source provenance, freshness, schema, counts, keys, and boundaries. A preview or sample describes only its covered scope; retrieve the complete records required by the claim using the source's pagination or persisted-result contract.
3. Check fitness. Test the relevant risks: missingness, duplicates, filter loss, join coverage/multiplication, changed definitions, partial periods, incompatible units, or shifted population/segment mix. State material exclusions and missing-value treatment. Narrow an unsupported claim instead of filling evidence gaps with speculation.
4. Compute reproducibly. Use transparent direct arithmetic for a few visible values when it is safely reviewable. Execute code, SQL, or spreadsheet formulas for bulk transformations, statistical inference, fitted models, precision-sensitive results, or when execution was requested. Reuse a bundled calculation below when its assumptions fit; otherwise use the simplest suitable implementation. Have the computation output the report-ready values with metric labels, units and denominators, including needed conversions and display rounding. Reuse those values in the answer; keep exact money/counts internally. Do not install a data-science stack merely for analysis.
5. Challenge the headline. Use an independent check capable of changing the conclusion: raw-component recomputation, reconciliation, record tracing, alternate grouping/baseline, or sensitivity analysis. Correct discrepancies; stop when further checks would not materially improve the answer.
6. Report at the evidence boundary. Lead with the result, its scope and practical meaning. Separate observation, interpretation, and recommendation. Put material assumptions, exclusions, and limitations beside their claims. Do not produce a checklist report or extra files merely to follow this loop.

Choosing uncertainty

Use quantitative uncertainty when the design supports it: a suitable interval for a sample estimate or experiment, validated prediction error/ranges for a forecast, or bounds/sensitivity analysis for incomplete evidence. Give the estimate, scale, sample basis, method, and material assumptions together. A complete in-scope deterministic total needs scope and validation, not an artificial sampling interval. When uncertainty cannot be quantified, explain the evidence gap and decision impact without an arbitrary numerical substitute.

Before responding, reconcile the answer's material numbers and labels with their derivations. Remove unsupported quantities introduced during explanation; in particular, remove any statistical quantity that did not pass the method gate, any numerical confidence that did not pass the confidence gate, and execute any precision-sensitive result that did not pass the precision gate.

Conditional references

• For a requested numerical confidence, binomial rate/difference intervals, missing binary outcome bounds, multiple-testing correction, or exact decimal totals/reconciliation, read calculations.md and execute the matching bundled script. Source extraction and business logic remain task-specific; these helpers do not define a universal confidence score.
• For sampling inference, experiments, forecasts, formal uncertainty, or causal analysis, read inference-and-uncertainty.md.
• For reviewing an existing analysis, reconciling conflicting sources, or validating a surprising or high-impact conclusion, read validation.md.