v0.2.0

dsh-probstat v0.2.0

Two new tools — the "what does this data actually say?" half of statistics, next to the existing distribution/interval math.

EN

• hypothesis_test — one-sample tests with the decision spelled out: mean_z (known sigma), mean_t (unknown sigma, Student-t with df = n − 1) and proportion (against p0, normal approximation). Each run returns the statistic, the standard error, all three p-values (two-sided / left / right), the critical value for the requested tail, and whether the null is rejected at alpha (default 0.05). The proportion kind adds a note when n·p0 < 5 instead of quietly handing back a p-value.
• sample_size — required sample size for a target margin of error: mean_z closed form n = (z·σ/E)², proportion via the Wald closed form (method: "wald", default) or the smallest n whose Wilson half-width meets E (method: "wilson", binary search). p defaults to 0.5, the conservative worst case; impossible targets return valid: false with the reason.

中文

• hypothesis_test：单样本假设检验——σ 已知走 z、σ 未知走 t（df = n−1）、单比例检验（对 p0 的正态近似）。一次给出统计量、标准误、三个 p 值（双侧/左/右）、所请求尾部的临界值与 α 判定（默认 0.05）。比例检验在 n·p0 < 5 时用 note 明确提示近似不可靠。
• sample_size：目标误差限所需样本量——均值闭式 n = (z·σ/E)²；比例可选 Wald 闭式（默认）或 Wilson 口径（二分求 Wilson 半宽 ≤ E 的最小 n）；p 默认 0.5（最保守），目标不可达时报错并说明原因。

Quality

• Tests 37 → 50; coverage 83.70% → 86.97% (baseline ratcheted up).
• Oracle moved to test/oracle/anchors.py (independent Python implementation: math.erf, math.comb, bisection, independent t and Wilson code) with a --check mode cross-validating published z/t-table constants. Every numeric assertion in the test file is generated there — including all legacy anchors.
• The TS implementation and the Python oracle agree to the documented precision; the one place they differ in the 8th significant digit (a squared critical value) is pinned at 5 digits with the reason in the test.

Install:

dsh plugin --profile web add github:TYEclipse/dsh-probstat