ads-test · 付费广告实验设计与结果解读

作者提供的示例，实际效果取决于你的输入和使用工具。

输入

为某广告组设计落地页 A/B 测试，声明样本量与测试时长假设，并给出显著性判断与上线决策。

输出

预期交付：版本化 JSON 的实验设置或结果解读，附通俗语言决策结论。（编辑说明，非实际运行结果）

---
name: ads-test
description: "Design and evaluate paid-ad experiments with hypotheses, randomization units, sample-size and duration assumptions, guardrails, platform experiment tools, analysis, and decision rules. Use for A/B test, split test, experiment design, hypothesis, statistical significance, sample size, test duration, or experiment readout."
---

# Paid Media Experiment

1. State the decision, causal hypothesis, treatment, control, randomization unit,
   population, primary metric, guardrails, minimum effect, and stopping rule.
2. Check platform constraints, overlapping experiments, conversion lag, seasonality,
   interference, and measurement quality.
3. Calculate sample and duration from declared assumptions; disclose approximations.
4. Change one decision surface unless the design explicitly estimates interactions.
5. Pre-register exclusions, quality checks, analysis, and decision thresholds.
6. For readout, verify assignment integrity and data completeness before estimating
   effect and uncertainty.
7. Return setup or readout in versioned JSON with a plain-language decision.

Do not repeatedly peek and stop on a favorable result, call underpowered noise a
winner, or generalize beyond the tested population.

先看看示例

输入

输出

方法说明