Experiment Design & Soundness Validation
Design rigorous experiments and validate methodologies before implementation
What You Can Do
Claude helps you design sound experiments from scratch, validate your methodology against established best practices, and identify potential flaws before you spend resources on implementation. You'll get a reproducible protocol, statistical power analysis, and a comprehensive checklist of control variables and potential confounds—ensuring your experiment produces reliable, actionable results.
Features
Evaluate your hypothesis for testability, falsifiability, and clarity. Claude identifies whether your hypothesis is well-formed and can be empirically tested.
Automated review of your experimental design to surface common pitfalls like hidden confounds, selection bias, measurement error, and threats to internal validity.
Calculate minimum sample size, effect size detection limits, and statistical power for your experiment design based on your chosen alpha and beta thresholds.
Systematically identify variables you must control, randomize, or match across groups to isolate your treatment effect from competing explanations.
Auto-generate detailed, step-by-step protocols that another researcher can follow to replicate your experiment with minimal ambiguity or interpretation.
Structured evaluation of experimenter bias, participant bias, demand characteristics, and environmental confounds specific to your design.
Comprehensive checklist covering internal, external, construct, and statistical conclusion validity—with recommendations to strengthen weak areas.
Example Output
Example 1: Hypothesis Validation Report
Hypothesis: "Larger font size improves reading comprehension"
✓ Testable: Yes — manipulable IV, measurable DV
✓ Falsifiable: Yes — can observe null or negative effect
⚠ Clarification needed: Define font size range and comprehension metric
Example 2: Methodological Flaw Review
Design: Compare comprehension (post-test only) between large-font and small-font groups
- 🔴 Risk: History confound — did groups experience same reading duration?
- 🔴 Risk: Selection bias — were participants randomly assigned?
- 🟡 Mitigation: Add pre-test comprehension measure; use randomized assignment
Example 3: Reproducible Protocol Excerpt
## Participant Assignment
1. Enroll 120 participants; randomize to 2 groups (n=60 each)
2. Use block randomization, block size 4, separate blocks per gender
3. Allocation: researcher draws sealed envelopes (pre-prepared by independent staff)
What's Included
- Experiment Design Template: Structured template covering hypothesis, IV/DV definitions, sample size justification, and validity threats.
- Methodology Validation Checklist: Point-by-point checklist to validate research design against gold standards for your field (RCT, observational, quasi-experimental, etc.).
- Statistical Power Calculator Guide: Step-by-step instructions for calculating power, sample size, and effect size detection—with references to standard tools and thresholds.
- Confound & Bias Assessment Matrix: Systematic framework to identify and rank threats to validity, with evidence-based mitigation strategies for each.
- Reproducible Protocol Generator: Guidelines and templates to write detailed, unambiguous protocols that enable replication and minimize researcher degrees of freedom.
- Validity Scorecard: Visual summary of your design's strengths and weaknesses across internal, external, construct, and statistical validity dimensions.
Who It's For
- Research Scientists
- Data Scientists & Analytics Leads
- Product Managers & UX Researchers
- Academic Researchers & PhD Students
- Quality Engineers & Reliability Teams
Best For
- Designing A/B tests and multivariate experiments
- Validating research protocols before IRB submission
- Creating reproducible experimental procedures
- Identifying methodological flaws early
- Calculating statistical power and sample size
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