result-interpretation

Result Interpretation

When to Use This Skill

• After running a statistical test
• When deciding if a hypothesis is supported or rejected
• When planning next steps based on results

Types of Results

1. Positive Finding (Hypothesis Supported)

Criteria:

• p-value < significance threshold (typically 0.05)
• Effect size is meaningful (not just statistically significant)
• Result makes biological sense

What to Do:

1. Calculate effect size - Don't rely on p-values alone

   • Cohen's d for t-tests
   • η² (eta-squared) for ANOVA
   • Correlation coefficient for associations

2. Record the finding

   update_knowledge_state(
       title="Clear, descriptive title",
       evidence="Statistical details: p-value, effect size, confidence interval",
       interpretation="Biological meaning"
   )
   

3. Search literature for validation

   • Does this align with known biology?
   • Are there papers supporting this mechanism?
   • What enzymes/pathways are involved?

4. Generate follow-up hypotheses

   • What explains this finding mechanistically?
   • What are the downstream consequences?
   • What conditions would reverse this effect?

Example:

Result: CDP-Choline Synthesis Index 35.4% higher in hypothermia (p=0.042, η²=0.29)

Interpretation:
- Statistically significant (p<0.05) ✓
- Large effect size (η²=0.29 is substantial) ✓
- Suggests Pcyt1 enzyme bottleneck

Action: Record finding, search "Pcyt1 regulation hypothermia"

2. Negative Finding (Hypothesis Rejected)

This is NOT a failure! Negative results are scientifically valuable.

Criteria:

• p-value > significance threshold
• OR effect size is trivial even if p<0.05

What to Do:

1. Document what was ruled out

   • Update hypothesis status to "rejected"
   • Note the p-value and confidence interval
   • Record why this hypothesis seemed plausible

2. Extract insights from the failure

   • What does this non-result tell us?
   • What alternative explanations remain?
   • Did we learn about the data structure?