AutoResearchClaw hypothesis-formulation
Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
install
source · Clone the upstream repo
git clone https://github.com/aiming-lab/AutoResearchClaw
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/aiming-lab/AutoResearchClaw "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/hypothesis-formulation" ~/.claude/skills/aiming-lab-autoresearchclaw-hypothesis-formulation && rm -rf "$T"
manifest:
.claude/skills/hypothesis-formulation/SKILL.mdsource content
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?