Agent Skills
› aiming-lab/AutoResearchClaw
› hypothesis-formulation
hypothesis-formulation
GitHub指导从观察中结构化生成可验证科学假设，包括设定零/备择假设、提出竞争性解释及设计实验预测。适用于需要严谨实验设计和假设检验的研究场景。
触发场景
制定可验证的科学假设
设计对照实验以区分竞争性解释
进行假设检验与实验规划
安装
npx skills add aiming-lab/AutoResearchClaw --skill hypothesis-formulation -g -y
SKILL.md
Frontmatter
{
"name": "hypothesis-formulation",
"metadata": {
"author": "researchclaw",
"version": "1.0",
"category": "experiment",
"priority": "3",
"references": "adapted from K-Dense-AI\/claude-scientific-skills",
"trigger-keywords": "hypothesis,prediction,mechanism,falsifiable,null,alternative,testable",
"applicable-stages": "7,8,9"
},
"description": "Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions."
}
Hypothesis Formulation Best Practice
Structured Hypothesis Development
- Start with a clear observation or pattern that requires explanation
- Review existing literature for known mechanisms and prior explanations
- Identify what is already established vs. what remains uncertain
- Formulate the hypothesis as a specific, testable statement
- Ensure the hypothesis is falsifiable — define what outcome would refute it
Hypothesis Format
- Null hypothesis (H0): There is no effect or no difference
- Alternative hypothesis (H1): There is a specific, directional effect
- State both explicitly; design experiments to reject H0
- Use "If... then... because..." structure for mechanistic hypotheses:
- If [independent variable is manipulated], then [predicted outcome], because [proposed mechanism]
Generating Competing Hypotheses
- Propose at least 2-3 plausible explanations for the same observation
- For each, identify unique predictions that distinguish it from alternatives
- Rank hypotheses by parsimony, consistency with prior evidence, and testability
- Design experiments that can discriminate between competing hypotheses
- Consider confounding variables that could produce the same observation
Testable Predictions
- Derive specific, measurable predictions from each hypothesis
- Define expected effect direction AND approximate magnitude
- Specify what experimental conditions would confirm vs. refute the prediction
- Identify potential confounds and plan controls to address them
- Ensure predictions are achievable with available methods and resources
Aligning with Experimental Design
- Map each hypothesis to a concrete experimental condition or comparison
- Ensure sample size is adequate to detect the predicted effect (power analysis)
- Pre-register hypotheses and analysis plans when possible
- Distinguish confirmatory (hypothesis-testing) from exploratory analyses
- Plan for both positive and null results — what will you conclude in each case?
版本历史
- e2e23c9 当前 2026-07-25 07:48
