Experimental Designs for Preclinical Neuroscience Experiments: Part I-Design Basics

Rigorous, statistically grounded experimental design is central to ethical and effective animal research. Foundational principles for statistically based Design of Experiments (DOE) were established over a century ago by Sir Ronald Fisher. They have since been augmented by modern computational tools that now enable researchers to implement designs that maximize scientific information and benefit while minimizing harms. However, many preclinical investigators are unfamiliar with formal DOE methods. Poorly designed experiments followed by inappropriate statistical analyses contribute to poor reproducibility, translational failure, and unnecessary animal use. This first paper in a three-part series introduces neuroscience researchers to the fundamentals of statistically based experimental design as a substitute for traditional two-group comparisons. Key components of a designed experiment are defined, along with the importance of correctly identifying experimental units to avoid pseudo-replication. Fisher's three essential design principles-randomization, replication, and blocking-are presented as nonoptional practices for controlling bias, managing variation, and ensuring valid statistical inferences. Particular emphasis is placed on probability-based random allocation, the use of validated computer-generated randomization plans, and the role of blocking in reducing nuisance variation. By embedding robust design principles early in study planning, researchers can produce reliable, reproducible, and ethically justifiable data. Subsequent papers in the series will expand on methods for controlling unwanted variation through blocking (Part 2) and outline flexible multivariable design strategies (Part 3). Worked examples and R code are included.