---
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.
