experimental-design

To generate a reproducible randomization schedule for a blocked experimental design, you create a seeded allocation table that maps treatment combinations to specific blocks. This preserves the correct unit of replication and ensures exact run order regeneration for preregistration.

Pseudoreplication is an experimental design error where repeated measurements from the same unit are treated as independent replicates. You avoid it in cluster-randomized studies by correctly identifying the experimental unit and allocating treatments at the proper cluster level.

You build a factorial design layout for screening nuisance variables by generating fractional factorial or Plackett-Burman layouts that separate treatment effects from confounding factors. This structured DOE allocation preserves study interpretability before data collection begins.

Yes, you can use Python to create a central composite design for response-surface optimization by leveraging libraries like pyDOE3 and pandas. This generates the structured run orders needed to map optimal factor levels and model response variables.

You should use stratified randomization instead of simple random allocation when you need to balance specific prognostic factors or nuisance variables across treatment groups. This prevents baseline confounding and ensures study comparisons remain interpretable before data analysis.

The best way to document an allocation schedule for preregistration is to export a seeded, reproducible table detailing the unit of randomization, blocking factors, and treatment combinations. This provides a transparent, fixed layout that prevents structural mistakes.