experiment-design

About experiment-design

Before collecting data, turn "what do we want to verify" into a statistically sound, reproducible, interpretable research design. Use when the user wants to design an experiment or study plan, choose a design type (completely randomized / randomized block / factorial / fractional factorial / crossover / repeated measures / cluster randomized / response surface), perform randomization and blocking, estimate sample size and statistical power, plan machine learning ablations or benchmarks, or create a statistical analysis plan and preregistration. Covers only "pre-collection design"; post-collection statistical/omics execution is left to data analysis skills.