prioritization

name	prioritization
description	Decide what to investigate next when you have multiple options
category	workflow

Prioritization

When to Use This Skill

• When you have multiple hypotheses and need to choose which to test first
• When you're deciding between exploring new areas vs deepening current investigation
• When iteration budget is running low and you need to focus

The Core Question

"What will provide the most scientific insight right now?"

Prioritization Framework

Option 1: Test a Hypothesis

Priority = Impact × Feasibility × Novelty

Rate each hypothesis:

• Impact (1-5): How central to research question?
• Feasibility (1-5): Can we test it with current data?
• Novelty (1-5): Is this a new insight?

Test highest-scoring hypothesis first.

Example:

H1: "Salvage flux is increased"
  Impact=4, Feasibility=4, Novelty=3 → Score=48

H2: "CMP→CDP bottleneck"
  Impact=5, Feasibility=5, Novelty=4 → Score=100 ⭐ Test this

H3: "Sex differences modulate effect"
  Impact=2, Feasibility=3, Novelty=2 → Score=12

Option 2: Explore Data

When to prioritize exploration:

• ✅ Early iterations (< 10% of budget)
• ✅ You're stuck and need fresh ideas
• ✅ Current hypotheses are all low-priority
• ✅ You haven't looked at key variable types yet

When NOT to explore:

• ❌ Late iterations (> 80% of budget)
• ❌ You have high-priority hypotheses ready to test
• ❌ You're avoiding testing a difficult hypothesis

Option 3: Search Literature

When to prioritize literature:

• ✅ Before testing a hypothesis (to inform design)
• ✅ After unexpected result (to understand mechanism)
• ✅ When hypothesis was rejected (to generate alternatives)
• ✅ Early iterations (build knowledge foundation)

When NOT to search:

• ❌ As procrastination (avoiding analysis)
• ❌ Late iterations when you should be synthesizing
• ❌ You already searched this topic

Option 4: Record/Synthesize Findings

When to prioritize synthesis:

• ✅ You've confirmed a finding but haven't recorded it
• ✅ Late iterations (> 70% of budget)
• ✅ You have 3+ findings that might be related
• ✅ You're approaching iteration limit

When NOT to synthesize:

• ❌ Early iterations (too early to connect dots)
• ❌ You only have 1-2 isolated findings
• ❌ High-priority hypotheses remain untested

Decision Tree

START: What should I do next?
│
├─> Do I have unrecorded findings?
│   YES → Record them now
│   NO → Continue
│
├─> Do I have high-priority hypotheses (score >50)?
│   YES → Test highest-scoring hypothesis
│   NO → Continue
│
├─> Is this early in investigation (<20% iterations)?
│   YES → Explore data OR search literature
│   NO → Continue
│
├─> Am I stuck or confused?
│   YES → Search literature for mechanisms
│   NO → Continue
│
├─> Is this late in investigation (>70% iterations)?
│   YES → Synthesize findings, test remaining high-priority hypotheses
│   NO → Continue
│
└─> Default: Generate new hypotheses OR explore unexplored data

Iteration-Specific Guidance

Early Phase (Iterations 1-10)

Priority:

1. Explore data structure
2. Search literature for domain knowledge
3. Identify major patterns
4. Test broad hypotheses

Example actions:

• Check data distributions and correlations
• Search "hypothermia metabolomics neuroprotection"
• Test for major group differences
• Generate initial hypothesis set

Middle Phase (Iterations 11-40)

Priority:

1. Test mechanistic hypotheses systematically
2. Follow up on positive findings
3. Refine understanding based on negative results
4. Build coherent story

Example actions:

• Test specific pathway hypotheses
• Calculate flux ratios and indices
• Search literature for unexpected results
• Connect findings across analyses

Late Phase (Iterations 41-50)

Priority:

1. Test remaining high-priority hypotheses
2. Record all confirmed findings
3. Synthesize into mechanistic model
4. Identify knowledge gaps

Example actions:

• Finish testing top-scoring hypotheses
• Ensure all findings are documented
• Prepare for final report
• Note what couldn't be answered

Common Prioritization Mistakes

❌ Analysis paralysis

• Don't overthink - pick something and test it
• You can always revise based on results

❌ Chasing rabbits

• Don't get distracted by low-priority tangents
• Stay focused on research question

❌ Perfectionism

• Don't wait for "perfect" hypothesis
• Test and learn from results

❌ Ignoring constraints

• Don't test untestable hypotheses
• Work with the data you have

❌ Linear thinking

• Don't feel obligated to finish one thread before starting another
• Sometimes pivoting is the right move

Example: Prioritization in Action

Situation: Iteration 15 of 50. Current state:

• 2 confirmed findings recorded
• 1 hypothesis rejected (salvage flux)
• 3 pending hypotheses generated
• Unexplored: lipid data

Options:

1. Test H2: "CMP→CDP bottleneck" (Score=100)
2. Test H3: "PUFA enrichment" (Score=75)
3. Test H4: "Sex difference" (Score=20)
4. Explore lipid data
5. Search literature on rejected hypothesis

Decision: Test H2 (highest priority hypothesis)

Rationale:

• High score (100)
• Directly addresses finding (CMP elevation)
• Feasible with current data
• Middle phase → focus on mechanistic hypotheses

Quick Reference

Test hypothesis → When you have high-priority (score >50) hypothesis ready

Explore data → When early (<20%) OR stuck

Search literature → Before testing hypothesis OR after unexpected result

Record findings → When you've confirmed something significant

Synthesize → When late (>70%) OR have 3+ related findings

Key Principle

Don't optimize for perfection. Optimize for scientific insight.

Each iteration should either:

1. Rule something out (negative result)
2. Confirm something (positive result)
3. Generate new ideas (exploration/literature)

If your action doesn't achieve one of these, reconsider.