empirical-systems-evaluation

To design multi-agent coordination benchmarks, define precise metrics like salvage latency and recovery fidelity, choose baselines, perform power-based sample size calculations, and pre-register an analysis plan with bootstrapped confidence intervals and effect sizes.

Benchmarking multi-agent recovery latency requires statistical decision trees to choose appropriate parametric or non-parametric tests, paired or independent analyses, and multiple-comparisons correction based on your data distribution and experimental design.

To measure inter-rater reliability for multi-agent salvage fidelity, apply human evaluation protocols featuring anchored rubrics, rater calibration sessions, and inter-rater reliability requirements to ensure valid blinded human ratings.

Sample size planning for coordination protocol experiments involves power-based calculations to ensure adequate statistical power, with bootstrap confidence interval procedures recommending B ≥ 10,000 resamples to validate effect sizes accurately.

Yes, comparing fault tolerance and scheduling algorithms requires calculating effect sizes with confidence intervals to quantify the practical significance of performance differences, supported by multiple-comparisons correction to address false positives.

Non-parametric tests are needed in multi-agent system evaluation when data violates normality assumptions, utilizing statistical decision trees to select the appropriate test based on data distribution, paired versus independent designs, and sample characteristics.