Bio-Logic: Scientific Reasoning Evaluation
Use structured frameworks to evaluate scientific claims, methodology, and evidence strength.
Instructions
- Identify the task (claim assessment, paper critique, study design review).
- Apply the relevant checklist below.
- Structure output using the provided format.
Critique Checklist
Use relevant sections based on the review scope. Skip items not applicable to the study type.
## Methodology
- [ ] Design matches research question (causal claim → RCT needed)
- [ ] Sample size justified (power analysis reported)
- [ ] Randomization/blinding implemented where feasible
- [ ] Confounders identified and controlled
- [ ] Measurements validated and reliable
## Statistics
- [ ] Tests appropriate for data type
- [ ] Assumptions checked
- [ ] Multiple comparisons corrected
- [ ] Effect sizes + CIs reported (not just p-values)
- [ ] Missing data handled appropriately
## Interpretation
- [ ] Conclusions match evidence strength
- [ ] Limitations acknowledged
- [ ] Causal claims only from experimental designs
- [ ] No cherry-picking or overgeneralization
## Red Flags
- [ ] P-values clustered just below .05
- [ ] Outcomes differ from registration
- [ ] Correlation presented as causation
- [ ] Subgroups analyzed without preregistrationClaim Assessment
- Identify claim type (causal, associational, descriptive).
- Match evidence to claim type.
- Check logical connection between data and conclusion.
- Ensure confidence matches evidence strength.
Claim strength ladder:
| Language | Requires |
|---|---|
| "Proves" / "Demonstrates" | Strong experimental evidence |
| "Suggests" / "Indicates" | Observational with controlled confounds |
| "Associated with" | Observational, no causal claim |
| "May" / "Might" | Preliminary or hypothesis-generating |
Output Format
## Summary
[1-2 sentences: What was studied and main finding]
## Strengths
- [Specific methodological strengths]
## Concerns
### Critical (threaten main conclusions)
- [Issue + why it matters]
### Important (affect interpretation)
- [Issue + why it matters]
### Minor (worth noting)
- [Issue]
## Evidence Rating
[GRADE level: High/Moderate/Low/Very Low with justification]
## Bottom Line
[What can/cannot be concluded from this evidence]Quick Reference
| Task | Action |
|---|---|
| Review a paper | Use the Critique Checklist below |
| Evaluate a claim | Use Claim Assessment steps |
| Assess evidence strength | See references/evidence.md |
| Identify biases | See references/biases.md |
| Statistical errors | See references/stats.md |
| Logical fallacies | See references/fallacies.md |
| Study design review | See references/design.md |
Input Requirements
- Manuscript or study summary
- Claim statements to evaluate
- Methods and results (as available)
Output
- Structured critique with strengths, concerns, and evidence rating
- Clear recommendation on claim strength and limitations
Quality Gates
- Claim type matches evidence type
- Key methodological risks are identified
- Evidence rating is justified with concrete issues
Examples
Example 1: Quick Paper Critique
Input: "Review this RCT on vitamin D and depression"
Output:
## Summary
RCT of 200 adults testing vitamin D supplementation for depression over 12 weeks.
## Strengths
- Randomized, double-blind, placebo-controlled
- Validated depression scale (PHQ-9)
- Adequate power analysis
## Concerns
### Critical
- 35% dropout, differential by group (attrition bias)
- ITT analysis not performed
### Important
- Single-site limits generalizability
## Evidence Rating
Moderate (downgraded from high due to attrition bias)
## Bottom Line
Suggestive but not conclusive due to differential attrition.Example 2: Claim Assessment
Input: "This study proves that coffee prevents Alzheimer's"
Assessment: If based on observational data, this is a correlation→causation error. Appropriate language: "Coffee consumption is associated with lower Alzheimer's risk."
Troubleshooting
Issue: Insufficient methods details Solution: Request missing design/statistics information before rating evidence.
Issue: Conflicting results across studies Solution: Report uncertainty and suggest stronger study designs for resolution.