experimental-design

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预防性设计建议能显著提升研究可解释性与统计效能。

这个 Skill 做什么

在数据收集前提供试验设计、随机化和区组安排的建议与流程。

在数据收集前帮你把试验设计搭好:选设计类型、随机化、分区(blocking)和处理组合,确保结论可解释且不被混淆。当你在安排受试者、样本分组、因子筛选、板式布局或考虑 crossover/重复测量等问题时就会用。特别强调前期决策的重要性,提供可执行的随机化和分配流程,避免事后无法补救的伪重复或混杂。

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Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statisti

研究检索实验设计随机化统计规划通用
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# Experimental Design

## Overview

The design of a study — how units are assigned to conditions, what is held constant, what is varied, and in what structure — determines what questions the data can answer. No analysis can rescue a confounded or pseudoreplicated design after the fact. This skill is about the decisions made *before* data collection: picking a design that isolates the effect of interest, randomizing to license causal claims, blocking to remove known nuisance variation, and structuring multi-factor experiments so effects are estimable rather than tangled together.

The three ideas behind almost every good design (Fisher's principles):
- **Randomization** — assign treatments at
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