hypothesis-testing-engine

State the claim clearly, then design a research protocol identifying data sources, sample size, confounding variables, and analysis methods. Gather evidence for and against, then issue a verdict with a confidence level and gaps to address.

The scientific method for claim validation involves forming a testable hypothesis, designing a controlled study, collecting data, analyzing results while accounting for confounders, and drawing a conclusion with an explicit confidence level.

Confounding variables are factors that correlate with both the cause and effect being studied, distorting conclusions. Identify them during study design by mapping alternative explanations for the observed relationship before gathering data.

AI can structure the evaluation by designing research protocols, searching existing studies, and summarizing evidence for and against a claim. The output includes a confidence level rather than absolute certainty, since conclusions depend on available data quality.

Automated hypothesis testing depends on the quality and availability of existing data and studies. It cannot run physical experiments or access proprietary datasets, so verdicts include what additional data would strengthen the conclusion.