📊 ab-test-calculator

输入两组数据，一句白话告诉你该不该上线。不做数据科学家也能看懂 A/B 测试。 Feed it two data sets — one plain-language verdict on whether to ship. No stats PhD required.

三种数据类型 · Three Data Types

类型 Type	检验方法 Test	场景 Use Case	输入 Input
连续型 Continuous	Welch's t + Cohen's d	加载时间、停留时长、评分<br>Load time, session duration, ratings	两组数字 或 均值±标准差<br>Raw values or mean±std
二项型 Binary	Chi-square + Cohen's h	转化率、点击率、留存率<br>Conversion rate, CTR, retention	转化数 / 总数<br>Conversions / total
计数型 Count	Mann-Whitney U	每日事件数、购买件数<br>Daily events, items purchased	两组事件计数<br>Per-user event counts

输出什么 · Output

📊 A/B Test Report

Group A:  12.50% conversion (120/1000)
Group B:  16.50% conversion (165/1000)

Difference:  +4.50pp   [+1.44pp, +7.56pp]
Relative:    +37.50%

χ² = 8.286   p = 0.004 **   Cohen's h = 0.13

⚠️ Significant but small effect.
   Real uplift, but modest magnitude.

一句话结论，不用猜 p 值啥意思。One-line verdict — no decoding p-values required.

技术亮点 · Tech Highlights

• 纯 Python 自实现：正态 CDF/PPF、不完全 Beta、不完全 Gamma → 精确的 p 值和置信区间，零依赖 Pure Python from scratch: normal CDF/PPF, incomplete Beta, incomplete Gamma → exact p-values & CIs, zero external deps
• Welch's t-test：不假设两组方差相等，比标准 t 检验更稳健 No equal-variance assumption — more robust than standard Student's t
• 效应量 + 统计功效：不只告诉你「是否显著」，还告诉你「差得值不值」和「样本量够不够」 Effect size + statistical power — not just "is it real?" but "does it matter?" and "did we test enough people?"
• 白话结论引擎：把 p 值、效应量、功效组合成一句人话 Plain-language verdict engine: synthesizes p-value, effect size, and power into one actionable sentence

触发词 · Triggers

「A/B测试」「AB测试」「显著性检验」「实验对比」「哪个版本更好」「转化率对比」「t检验」「卡方检验」「统计功效」「ab test」