// Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.
| name | hypothesis-generation |
| description | Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains. |
Hypothesis generation is a systematic process for developing testable explanations. Formulate evidence-based hypotheses from observations, design experiments, explore competing explanations, and develop predictions. Apply this skill for scientific inquiry across domains.
This skill should be used when:
Follow this systematic process to generate robust scientific hypotheses:
Start by clarifying the observation, question, or phenomenon that requires explanation:
Search existing scientific literature to ground hypotheses in current evidence. Use both PubMed (for biomedical topics) and general web search (for broader scientific domains):
For biomedical topics:
For all scientific domains:
Search strategy:
references/literature_search_strategies.md for detailed search techniquesAnalyze and integrate findings from literature search:
Develop 3-5 distinct hypotheses that could explain the phenomenon. Each hypothesis should:
Strategies for generating hypotheses:
Assess each hypothesis against established quality criteria from references/hypothesis_quality_criteria.md:
Testability: Can the hypothesis be empirically tested? Falsifiability: What observations would disprove it? Parsimony: Is it the simplest explanation that fits the evidence? Explanatory Power: How much of the phenomenon does it explain? Scope: What range of observations does it cover? Consistency: Does it align with established principles? Novelty: Does it offer new insights beyond existing explanations?
Explicitly note the strengths and weaknesses of each hypothesis.
For each viable hypothesis, propose specific experiments or studies to test it. Consult references/experimental_design_patterns.md for common approaches:
Experimental design elements:
Consider multiple approaches:
For each hypothesis, generate specific, quantitative predictions:
Use the template in assets/hypothesis_output_template.md to present hypotheses in a clear, consistent format:
Standard structure:
Ensure all generated hypotheses meet these standards:
hypothesis_quality_criteria.md - Framework for evaluating hypothesis quality (testability, falsifiability, parsimony, explanatory power, scope, consistency)experimental_design_patterns.md - Common experimental approaches across domains (RCTs, observational studies, lab experiments, computational models)literature_search_strategies.md - Effective search techniques for PubMed and general scientific sourceshypothesis_output_template.md - Structured format for presenting hypotheses consistently with all required sectionsGenerate testable hypotheses from observations. Use for formulating research questions, designing experiments, exploring competing explanations, developing predictions, and proposing mechanisms. Triggers: generate hypothesis, formulate hypothesis, research question, experimental design, competing explanations, mechanistic hypothesis, testable predictions, scientific inquiry.
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