bio-logic

name	bio-logic
description	Evaluates scientific research rigor using systematic frameworks. Assesses methodology, statistics, biases, and evidence quality. Use when reviewing papers, critiquing claims, designing studies, rating evidence strength (GRADE/Cochrane ROB), checking study design, statistical critique, or risk of bias assessment.

Bio-Logic: Scientific Reasoning Evaluation

Instructions

1. Identify the task using Quick Reference below
2. Use the appropriate framework from this file or references
3. Adapt depth to context - use full checklists for thorough reviews, key items for quick assessments
4. Structure output using the Output Format template

Quick Reference

Navigate to the right tool for your task:

Task	Location
Review a paper	Critique Checklist below
Evaluate a claim	Claim Assessment below
Assess evidence strength	references/evidence.md
Identify biases	references/biases.md
Spot statistical errors	references/stats.md
Detect logical fallacies	references/fallacies.md
Design/review a study	references/design.md

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 preregistration

Claim Assessment

For any scientific claim:

1. Identify claim type: Causal, associational, or descriptive?
2. Match evidence to claim: Does study design support this claim type?
3. Check logical connection: Do conclusions follow from data?
4. Evaluate proportionality: Is confidence matched to evidence strength?

Causal claims require: Temporal precedence + covariation + alternative explanations ruled out (ideally experimental manipulation).

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

Structure critiques as:

## 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]

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: Claim uses causal language ("prevents") but if based on observational data, this is a correlation→causation fallacy. Would need RCT or strong observational evidence (large effect, dose-response, controlled confounds) to support causal claim. Appropriate language: "Coffee consumption is associated with lower Alzheimer's risk."

Principles

1. Be constructive - Identify strengths, suggest improvements
2. Be specific - Quote problematic statements, cite specific issues
3. Be proportionate - Match criticism severity to impact on conclusions
4. Be consistent - Same standards regardless of whether you agree with findings
5. Distinguish - Data vs interpretation, correlation vs causation, statistical vs practical significance

Reference Materials

Detailed frameworks for specific evaluation tasks:

• references/evidence.md - GRADE system, evidence hierarchy, validity types, Bradford Hill criteria
• references/biases.md - Bias taxonomy with detection strategies
• references/stats.md - Statistical pitfalls and correct interpretations
• references/fallacies.md - Logical fallacies in scientific arguments
• references/design.md - Experimental design checklist