hypothesis-generation

name	hypothesis-generation
description	Generate testable hypotheses from data patterns and literature
category	workflow

Hypothesis Generation

When to Use This Skill

• After identifying an interesting pattern in the data
• When a previous hypothesis was rejected (need to generate alternatives)
• At the start of investigation (bootstrap from literature)
• When you're stuck and need fresh ideas

The Process

1. Review Current Knowledge

What patterns have been observed?

• Check the knowledge graph summary
• What group differences exist?
• What correlations are surprising?
• What contradicts expectations?

What has been tested already?

• Which hypotheses were supported?
• Which were rejected? (Don't repeat these!)
• What did negative results tell us?

What does literature say?

• Search PubMed for relevant papers
• Extract known mechanisms
• Identify knowledge gaps

2. Formulate Specific, Testable Hypotheses

Good hypotheses have this structure: "X causes Y via mechanism Z"

Examples:

• ✅ Good: "Hypothermia increases nucleotide salvage flux by upregulating APRT enzyme activity"
• ❌ Too vague: "Metabolism changes in hypothermia"
• ❌ Not testable: "The brain adapts to cold"

Requirements:

• Must be testable with available data
• Must be falsifiable (can prove it wrong)
• Should suggest a specific analysis
• Should have mechanistic basis (not just correlation)

3. Prioritize Hypotheses

Score each hypothesis on:

Impact (1-5): How central to the research question?

• 5 = Directly explains the core phenotype
• 3 = Fills in a mechanistic detail
• 1 = Minor tangential observation

Feasibility (1-5): Can we test it with current data?

• 5 = Have all required variables
• 3 = Can construct proxy measure
• 1 = Missing critical data

Novelty (1-5): Is this a new insight?

• 5 = No one has asked this before
• 3 = Refinement of known mechanism
• 1 = Well-studied question

Coherence (1-5): Fits with existing findings?

• 5 = Explains contradictions or connects findings
• 3 = Extends current model
• 1 = Orthogonal to other findings

Total Priority Score = Impact × 0.4 + Feasibility × 0.3 + Novelty × 0.2 + Coherence × 0.1

Test highest-scoring hypotheses first.

4. Design the Test

For each hypothesis, specify:

• What statistical test to use
• What variables to compare
• What result would support the hypothesis
• What result would reject it

Example Workflow

Observation: "CMP is elevated in hypothermia (FC=1.8, p<0.01)"

Step 1: Search literature

search_pubmed("CMP metabolism nucleotide salvage")

Finds: CMP is product of nucleotide salvage pathway

Step 2: Generate hypotheses

• H1: "Salvage flux is increased" (upstream cause)
• H2: "CMP→CDP conversion is blocked" (downstream bottleneck)
• H3: "CMP degradation is reduced" (clearance issue)

Step 3: Prioritize

• H1: Impact=4, Feasibility=4, Novelty=3, Coherence=4 → Score=3.8
• H2: Impact=5, Feasibility=5, Novelty=4, Coherence=4 → Score=4.6 ⭐
• H3: Impact=3, Feasibility=2, Novelty=2, Coherence=3 → Score=2.6

Step 4: Test H2 first Calculate CDP-Choline Synthesis Index = CMP / CDP-Choline Compare across groups

Common Pitfalls to Avoid

❌ Don't repeat rejected hypotheses

• Check the knowledge graph for what's already been ruled out

❌ Don't cherry-pick

• Test hypotheses systematically, not just ones likely to succeed

❌ Don't ignore negative results

• Failed hypotheses are valuable - they constrain the solution space

❌ Don't generate untestable hypotheses

• If you can't test it with current data, it's not useful now

When to Stop

You have enough hypotheses when:

• You have 2-3 high-priority (score >4.0) hypotheses ready to test
• You've covered the main alternative explanations
• Further brainstorming is giving diminishing returns

Don't generate 20 hypotheses - focus on quality over quantity.