validation

To design an experimental protocol to validate a research hypothesis, you specify the design type, independent and dependent variables, controls, sample size with power analysis, timeline, and step-by-step protocol. This structured output satisfies requirements for preregistration and replication.

Supported experimental design types for hypothesis testing include randomized controlled trials (RCTs), quasi-experimental designs, longitudinal studies, and in silico simulations. These options accommodate varied research contexts across multiple disciplines.

Sample size and power analysis for an experiment are integrated directly into the complete protocol specification. The design process includes these calculations alongside independent variables, dependent variables, and controls to ensure defensible study plans.

Yes, you can use this experimental design tool for in silico simulations and longitudinal studies. It supports these specific design formats alongside RCTs and quasi-experiments to fit varied hypothesis-driven research contexts.

Automated experimental protocol designs provide explicit guardrails for validity threats but require a clearly defined hypothesis as input. They focus on generating structured study plans and do not execute the actual experiments or collect the research data.