experimental-design

To create a randomized experiment design with blocking, you specify your treatment factors, unit of randomization, and nuisance variables like batch or site. The Skill then generates a reproducible, seeded allocation table that balances known nuisance factors across treatments.

Pseudoreplication in experimental design occurs when technical replicates are mistakenly treated as independent biological replicates during analysis. You avoid it by matching the unit of randomization to the unit of inference, ensuring correct handling of clustering and nesting in your study layout.

You generate a fractional factorial DOE matrix by requesting a screening design for your specified factors. The Skill builds alias-aware DOE matrices, including Plackett-Burman or fractional designs, to identify main effects while systematically reducing the required number of experimental runs.

Yes, you can use Python to plan crossover, split-plot, and cluster-randomized designs. The Skill leverages numpy, pandas, and pyDOE3 to correctly handle clustering, nesting, and replication, producing balanced allocation schedules for complex experimental structures.

You need a central composite or Box-Behnken design for response surface optimization rather than simple factor screening. These designs are used when you need to model curvature and find optimal treatment conditions, whereas full factorial layouts are better for identifying main factor effects.

Your factorial design has confounding when aliasing structures overlap main effects with interactions, often due to insufficient runs. You fix it by generating alias-aware DOE matrices that systematically manage the confounding pattern, preventing main effects from being confounded with each other.