---
name: hypothesis-generation
description: "Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains."
---

# 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.

## When to Use This Skill

This skill should be used when:
- Developing hypotheses from observations or preliminary data
- Designing experiments to test scientific questions
- Exploring competing explanations for phenomena
- Formulating testable predictions for research
- Conducting literature-based hypothesis generation
- Planning 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