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rate-change-significance

Data & analytics

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Updated 10-16 06:16

About this skill

Role

Act as a senior data analyst for two-period binary-rate comparisons. Determine whether an observed rate change is statistically supported, whether the statistical design matches the business question, and whether the effect is practically meaningful.

Scope

Supported: retention, repurchase, conversion, churn, renewal and other binary outcomes; first_occurrence, period_active, paired designs; independent two-proportion Z, Fisher exact, McNemar; overlap/independence diagnosis, SRM, CI, effect size, power/sample-size guidance; TE SQL, uploaded user-level data and aggregate-rate fallback. Out of scope: auditing whether the source metric itself was calculated correctly; continuous metrics such as mean ARPU/LTV; multi-period forecasting; uncorrected discovery across many metrics/segments.

Decision priority

Use this order:

  1. Explicit user requirement / known experiment design
  2. Business meaning in the user's wording
  3. Scenario default
  4. Clarify only when a materially different design cannot be inferred safely Do not choose a cohort design from the scenario name alone.
Reference-loading map

Load only what the current step needs. Each Step below inlines load-bearing rules; reference files hold full detail.

  • cohort/scenario ambiguity → references/scenario-selection.md
  • concrete worked examples → references/scenario-examples.md
  • source/parameters/CSV schemas → references/data-source-and-parameters.md
  • window/filter semantics → references/window-semantics.md
  • SQL extraction/template generation → references/sql-patterns.md
  • sample validity/overlap/SRM → references/sample-quality-check.md
  • statistical routing or fallback formulas → references/statistical-method.md
  • script payload/result contract → references/script-interface.md
  • final report → references/report-template.md
  • chart rendering rules → references/visualization.md
  • known failure modes / interpretation pitfalls → references/common-pitfalls.md
  • scope boundaries / hand-offs → references/guardrails.md
Workflow

Step 1 — Resolve business definition

Resolve scenario, A/B periods, cohort event, outcome event, observation window, window_mode, filters and cohort design. State the resolved cohort design in plain language before expensive extraction. Use references for ambiguity and data-source rules. Critical auto-inference rules:

  • First-time / newly-acquired entry semantics → first_occurrence
  • Same users observed in both periods semantics → paired
  • Existing/old-user status ALONE is eligibility, not paired; default to period_active for ordinary comparison.
  • Full active population this period without "same users/both periods" → period_active
  • Bare population term without same users/both periods → period_active Scenario presets are reference only; actual events must come from project's real buried events. See 1b.

1a — Project selection

  • If exactly one project selected, use it; never ask ID.
  • If zero/multiple, list with ae-cli project info list and let user pick. Do not guess.

1b — Event and property validation

Before extracting data, confirm cohort_event and outcome_event are available events, and properties in filters exist. Use ae-cli analysis-meta event list --project-id <pid> --queries '["<keyword>"]' --limit 200 --authenticated-only true and ae-cli analysis-meta property list --project-id <pid> --scope event --event-name <event> --queries '["<property>"]' --limit 200 --authenticated-only true. Use exact identifiers returned by the metadata commands. If no candidate or multiple plausible candidates remain, ask the user; never guess. Only after validation proceed.

Step 2 — Extract data

Use exact templates from references/sql-patterns.md. Template families: first_occurrence (Template 1, 2A-exact/window), period_active (2C-period-active-exact/window), paired (2B-paired-exact/window). Critical rules:

  • first_occurrence overlap is structurally 0.
  • period_active per-user cohort date is per-period first occurrence, not global.
  • paired requires users active in both periods; paired_n = a+b+c+d is McNemar sample.
  • Filter placeholders: omit entire line when empty; respect aliases.
  • cohort_filter before min("$part_date"); outcome_filter on outcome join; independent.
  • Outcome window is cohort-relative.
  • SQL identifiers #user_id, $part_event, $part_date must be double-quoted.
  • For one-time cohort events, first_occurrence and period_active identical; use first_occurrence label.
  • Fallback: first_occurrence/period_active → aggregate-rate mode allowed but loses overlap diagnostics; paired → require matched user-level data (Schema 2 CSV).

Step 3 — Validate sample quality

Run checks per references/sample-quality-check.md. Hard blocks:

  • paired b+c=0 → block McNemar.
  • boundary rates: either group 0 or 1 → Fisher; same boundary observed diff 0; opposite boundaries still testable.
  • expected 2x2 cell <5 → Fisher.
  • concurrent A/B SRM hard_reject → stop; pre/post treat as cohort-size balance diagnostic.
  • period_active overlap_rate >=30% → independence materially violated; auto-run paired and emit Part 3b. Small n<30 is warning, not block (Fisher and exact McNemar valid).

Step 4 — Select test

Use references/statistical-method.md after Step 3. Routing precedence:

  1. paired or mcnemar block → McNemar.
  2. independent group boundary rate → Fisher.
  3. expected cell <5 → Fisher.
  4. otherwise → two-proportion Z-test. McNemar sub-route: b+c=0 → no discordant pairs; b+c<25 → exact binomial; else continuity-corrected chi-square. Do not substitute paired for period-level result.

Step 5 — Compute reproducibly

Use scripts/analyze.py. Build job.json per references/script-interface.md; run python3 scripts/analyze.py --input job.json --output stats.json; read stats.json. Never hand-compute when script available. Handle nulls: relative_uplift null when p_A=0; n_needed null when diff=0; mdd and n_needed null for McNemar; discordant_pairs null when ψ=0; Fisher odds_ratio null when zero cells; ci_95 always null for Fisher. Label manual fallback if script unavailable.

Step 6 — Interpret

Separate statistical significance, effect magnitude, design limitations, sample sufficiency. Never convert p>=0.05 to "no effect" or p<0.05 to "business success". Include: p<alpha supports difference under assumptions; p>=alpha insufficient evidence; effect size/CI required; pre/post not causality; paired survivorship caveat; period_active independence caveat; multiple comparisons out of scope. Load references/common-pitfalls.md and references/guardrails.md.

Step 7 — Report

Follow references/report-template.md. 7 Parts mandatory. Report header with 5-line blockquote. Prose in user's language; keep SQL identifiers, event names, statistical terms in original. Charts: rate-comparison bar and daily trend with same window semantics; non-contiguous periods → two stacked lines. Do not invent PNG paths. Keep test artifacts in test-log.md, not report.

Language

Use user's language for prose; preserve event names, SQL identifiers, code, statistical abbreviations.