alterlab-hypothesis-gen

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

Does NOT Trigger

Scenario	Use Instead
Open-ended, blue-sky ideation with no specific observation to explain yet	`alterlab-scientific-brainstorm`
Automated LLM-driven hypothesis generation and testing over a tabular dataset (HypoGeniC)	`alterlab-hypogenic`
A standalone systematic review or PRISMA evidence synthesis as the deliverable	`alterlab-literature-review`
Grading the evidence quality or design flaws of existing claims	`alterlab-scientific-thinking`
Freezing hypotheses and the analysis plan in a pre-registration before data collection	`alterlab-preregistration-discipline`

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

Generate a professional LaTeX document using the template in assets/hypothesis_report_template.tex. The report should be well-formatted with colored boxes for visual organization and divided into a concise main text with comprehensive appendices.

Document Structure:

Main Text (about 4 pages):

Executive Summary — brief overview in summarybox (0.5-1 page)
Competing Hypotheses — each hypothesis in its own colored box with a brief mechanistic explanation and key evidence (2-2.5 pages for 3-5 hypotheses)
Testable Predictions — key predictions in predictionbox (0.5-1 page)
Critical Comparisons — priority comparisons in comparisonbox (0.5-1 page)

Keep the main text to the essentials; everything else goes to the appendices:

Appendix A: Comprehensive literature review with extensive citations
Appendix B: Detailed experimental designs with full protocols
Appendix C: Quality assessment tables and detailed evaluations
Appendix D: Supplementary evidence and analogous systems

Colored Box Usage (environments from hypothesis_generation.sty):

hypothesisbox1 through hypothesisbox5 — one per competing hypothesis (blue, green, purple, teal, orange)
predictionbox — testable predictions (amber)
comparisonbox — critical comparisons (steel gray)
evidencebox — supporting-evidence highlights (light blue)
summarybox — executive summary (blue)

Each hypothesis box contains:

Mechanistic Explanation: 1-2 brief paragraphs (6-10 sentences) explaining how and why
Key Supporting Evidence: 2-3 bullet points with citations (most important evidence only)
Core Assumptions: 1-2 critical assumptions

Page overflow. The boxes in hypothesis_generation.sty are not breakable tcolorboxes, so a box taller than the space left on the page runs off the bottom and the PDF becomes unreadable. Keep each hypothesis box to about 0.6 page (15-20 lines); move longer mechanism detail and extra evidence to Appendix A. Before each box, start a fresh page with \newpage if less than ~0.6 page remains, and put \newpage between major appendix sections.

\newpage
\begin{hypothesisbox1}[Hypothesis 1: Title]
% mechanistic explanation, 2-3 evidence bullets, 1-2 assumptions
\end{hypothesisbox1}

Citation Requirements:

Main text: 10-15 key citations for the most important evidence only
Appendix A: comprehensive coverage of the relevant literature (typically 40-70 citations for a well-studied phenomenon)

Cite only sources you actually retrieved and checked during the literature search — never invent or pad references to reach a target, because a fabricated citation undermines the whole report. For a sparse literature, fewer verified citations are the right answer. Use \citep{author2023} for parenthetical citations.

LaTeX Compilation:

The template requires XeLaTeX or LuaLaTeX (it loads fontspec):

xelatex hypothesis_report.tex
bibtex hypothesis_report
xelatex hypothesis_report.tex
xelatex hypothesis_report.tex

Required packages: hypothesis_generation.sty must be in the same directory or on the LaTeX path. It requires tcolorbox, xcolor, fontspec, fancyhdr, titlesec, enumitem, booktabs, and natbib.

Quick Reference: See assets/FORMATTING_GUIDE.md for detailed examples of all box types, color schemes, and common formatting patterns.

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_generation.sty - LaTeX style package providing colored boxes, professional formatting, and custom environments for hypothesis reports
hypothesis_report_template.tex - Complete LaTeX template with main text structure and comprehensive appendix sections
FORMATTING_GUIDE.md - Quick reference guide with examples of all box types, color schemes, citation practices, and troubleshooting tips

Related Skills

alterlab-literature-review — for a standalone systematic review / PRISMA synthesis when the deliverable is the review itself rather than a focused search to ground hypotheses.
alterlab-venue-templates — for venue-specific LaTeX templates and submission formatting when turning a hypothesis report into a manuscript.
alterlab-scientific-writing — for drafting the resulting manuscript in flowing IMRAD prose.

Part of the AlterLab Academic Skills suite.