Experiment Runner

To run an A/B test comparing LLM model tiers, specify your hypothesis, define control and treatment configurations, set the sampling size, and collect metrics. The experiment outputs a structured JSON report with significance testing and effect size.

Validating prompt modifications requires designing controlled experiments with clear control and treatment groups. The system calculates sample size based on statistical power and applies t-tests or chi-square analysis to prove changes with statistical confidence.

Sample size calculation for an A/B test is based on statistical power to guarantee sufficient data collection. You must specify the hypothesis and metric collection parameters during experiment design to ensure the test detects meaningful effects.

Yes, you can evaluate system parameter settings before deployment by formulating a performance hypothesis and running a controlled experiment. It compares configurations using automated data collection and statistical analysis to validate improvements.

Statistical analysis methods applied to the experiment data include t-tests, chi-square tests, and effect size calculations. These methods process the automated data collection to deliver a structured report with significance testing.

A controlled experiment is needed because development decisions often rely on intuition or isolated observations, causing unreliable improvements. Systematic hypothesis validation ensures parameter changes are proven with statistical confidence before deployment.