hypothesis-testing-engine

Testing a scientific claim requires designing a complete research protocol that identifies data sources, calculates required sample sizes, controls for confounding variables, and specifies analysis methods before execution. This Skill automates that process: it builds the protocol, gathers data, runs analyses, and delivers a verdict with confidence levels.

Yes. The scientific method applies across scientific, policy, and product claims. This Skill designs and executes research protocols for any claim type by identifying appropriate data sources, defining controls, and rendering evidence-based verdicts with confidence levels and recommendations for strengthening conclusions.

Confounding variables are factors other than your hypothesis that could explain observed results. This Skill identifies confounders during research design by analyzing your claim, data sources, and study context, then accounts for them in the analysis to isolate the true effect and increase result validity.

Data requirements depend on your hypothesis and research design. This Skill determines the necessary data sources, calculates minimum sample size for statistical power, and specifies collection methods during protocol design, then automates gathering and analysis to test your claim reliably.

Confidence depends on evidence quality, sample size, and effect size. This Skill outputs a confidence level for each verdict based on the data gathered and analysis performed, plus recommendations for additional data collection to strengthen conclusions if confidence is insufficient.

Claim testing uses the scientific method: define hypothesis and protocol first, then gather data and analyze. Exploratory analysis searches data for patterns after collection. This Skill enforces the rigorous approach—pre-specifying design, controls, and analysis methods—to reduce bias and deliver reliable verdicts.