Add statistics and A/B significance testing#273
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ab_locator ranks by raw success rate and run_history stores durations, but nothing computed percentiles or whether a difference is statistically significant. Add descriptive stats + percentiles, a two-proportion z-test with CI, Welch's t-test (exact t-distribution p-value via the incomplete beta, no SciPy), Cohen's d and a 2x2 chi-square. Normal CDF exact via erf; validated against textbook values. Wired through the facade, AC_describe_stats and AC_ab_significance executor commands, MCP tools and the Script Builder.
Up to standards ✅🟢 Issues
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| Metric | Results |
|---|---|
| Complexity | 68 |
| Duplication | 0 |
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Summary
ab_locatorranks strategies by raw success rate andrun_historystores durations, but nothing computed percentiles or told you whether a difference is statistically significant (vs. noise). This adds the analysis layer.percentile(values, q, method=)/describe(values)— summary stats + p50/p90/p95/p99.two_proportion_z_test(a_conv, a_n, b_conv, b_n)— conversion-rate test, pooled SE for the test + unpooled SE for the CI; returns{z, p_value, significant, diff, ci_low, ci_high}.welch_t_test(a, b)— continuous metrics, Welch–Satterthwaite df, exact t-distribution p-value via the regularized incomplete beta (no SciPy).cohens_d(a, b)— effect size;chi_square_2x2(...)— df=1 chi-square.Normal CDF exact via
math.erf. Validated against textbook values, including the chi²=z² identity. Pure stdlib (math+statistics).Five-layer wiring
je_auto_control/utils/stats/__init__.py+__all__AC_describe_stats,AC_ab_significanceac_describe_stats,ac_ab_significanceTests & docs
test/unit_test/headless/test_stats_batch.py(14 tests against textbook values: percentiles, z-test 90/200 vs 110/200 → p=0.0455, Welch sig/non-sig, chi²=z²)Lint clean: ruff / pylint / bandit / radon (no function CC > 10).