hypothesis-generation

name	hypothesis-generation
description	Generate 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.

Scientific Hypothesis Generation

Overview

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.

Table of Contents

• When to Use
• Workflow
• Quality Standards
• Resources

When to Use

Use this skill to:

• Develop hypotheses from observations or preliminary data
• Design experiments to test scientific questions
• Explore competing explanations for phenomena
• Formulate testable predictions for research
• Conduct literature-based hypothesis generation
• Plan mechanistic studies across scientific domains

Workflow

Follow this systematic process to generate robust scientific hypotheses:

1. Understand the Phenomenon

Start by clarifying the observation, question, or phenomenon that requires explanation:

• Identify the core observation or pattern that needs explanation
• Define the scope and boundaries of the phenomenon
• Note any constraints or specific contexts
• Clarify what is already known vs. what is uncertain
• Identify the relevant scientific domain(s)

2. Conduct Comprehensive Literature Search

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:

• Use WebFetch with PubMed URLs to access relevant literature
• Search for recent reviews, meta-analyses, and primary research
• Look for similar phenomena, related mechanisms, or analogous systems

For all scientific domains:

• Use WebSearch to find recent papers, preprints, and reviews
• Search for established theories, mechanisms, or frameworks
• Identify gaps in current understanding

Search strategy:

• Begin with broad searches to understand the landscape
• Narrow to specific mechanisms, pathways, or theories
• Look for contradictory findings or unresolved debates
• Consult references/literature_search_strategies.md for detailed search techniques

3. Synthesize Existing Evidence

Analyze and integrate findings from literature search:

• Summarize current understanding of the phenomenon
• Identify established mechanisms or theories that may apply
• Note conflicting evidence or alternative viewpoints
• Recognize gaps, limitations, or unanswered questions
• Identify analogies from related systems or domains

4. Generate Competing Hypotheses

Develop 3-5 distinct hypotheses that could explain the phenomenon. Each hypothesis should:

• Provide a mechanistic explanation (not just description)
• Be distinguishable from other hypotheses
• Draw on evidence from the literature synthesis
• Consider different levels of explanation (molecular, cellular, systemic, population, etc.)

Strategies for generating hypotheses:

• Apply known mechanisms from analogous systems
• Consider multiple causative pathways
• Explore different scales of explanation
• Question assumptions in existing explanations
• Combine mechanisms in novel ways

5. Evaluate Hypothesis Quality

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.

6. Design Experimental Tests

For each viable hypothesis, propose specific experiments or studies to test it. Consult references/experimental_design_patterns.md for common approaches:

Experimental design elements:

• What would be measured or observed?
• What comparisons or controls are needed?
• What methods or techniques would be used?
• What sample sizes or statistical approaches are appropriate?
• What are potential confounds and how to address them?

Consider multiple approaches:

• Laboratory experiments (in vitro, in vivo, computational)
• Observational studies (cross-sectional, longitudinal, case-control)
• Clinical trials (if applicable)
• Natural experiments or quasi-experimental designs

7. Formulate Testable Predictions

For each hypothesis, generate specific, quantitative predictions:

• State what should be observed if the hypothesis is correct
• Specify expected direction and magnitude of effects when possible
• Identify conditions under which predictions should hold
• Distinguish predictions between competing hypotheses
• Note predictions that would falsify the hypothesis

8. Present Structured Output

Use the template in assets/hypothesis_output_template.md to present hypotheses in a clear, consistent format:

Standard structure:

1. Background & Context - Phenomenon and literature summary
2. Competing Hypotheses - Enumerated hypotheses with mechanistic explanations
3. Quality Assessment - Evaluation of each hypothesis
4. Experimental Designs - Proposed tests for each hypothesis
5. Testable Predictions - Specific, measurable predictions
6. Critical Comparisons - How to distinguish between hypotheses

Quality Standards

Ensure all generated hypotheses meet these standards:

• Evidence-based: Grounded in existing literature with citations
• Testable: Include specific, measurable predictions
• Mechanistic: Explain how/why, not just what
• Comprehensive: Consider alternative explanations
• Rigorous: Include experimental designs to test predictions

Resources

references/

• 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 sources

assets/

• hypothesis_output_template.md - Structured format for presenting hypotheses consistently with all required sections