Reduce or not reduce: a practical guide to calculate sample size for various experimental design in studies involving...

The ethical imperative to reduce the use of animals in scientific experimentation has become increasingly prominent, driven by societal expectations, regulatory frameworks, and concerns regarding the reproducibility of preclinical research. While the principle of Reduction, one of the 3Rs originally defined by Russell and Burch, is widely endorsed, its practical implementation remains challenging. In particular, inappropriate experimental designs, inadequate statistical planning, and insufficient reporting standards continue to undermine both scientific validity and ethical justification, often leading either to the unnecessary use of animals or to underpowered studies whose results lack reliability. This review provides an updated and practical framework for determining the appropriate number of animals required in experimental biology and pharmacology. Building on earlier methodological recommendations, it emphasizes the central role of rigorous study planning, including the clear definition of objectives and primary endpoints, appropriate experimental design, randomization, blinding, and the judicious use of control groups. Special attention is given to factorial designs and the inclusion of interactions, which often allow substantial reductions in animal numbers without compromising statistical power. The review also discusses conditional procedures, pilot studies, interim analyses, and the frequent confusion between biological replicates, technical replicates, and true experimental replication. The statistical foundations of a priori sample size calculation are revisited, including the concepts of Type I and Type II errors, effect size, variability, and corrections for multiple comparisons. Practical guidance is provided for common experimental contexts, including comparisons of continuous and categorical outcomes, analysis of variance, survival analysis, pharmacokinetic and pharmacodynamic studies, and dose-response experiments. Emerging approaches, such as sequential and Bayesian methods, are also discussed with respect to their potential for reduction and their limitations. Finally, this review highlights the ethical and scientific risks associated with excessive reduction leading to underpowered studies. It argues that optimal reduction does not mean using as few animals as possible, but rather using the right number, justified by sound experimental design and statistical reasoning. By promoting transparent planning and robust methodology, this work aims to support both ethical responsibility and scientific rigor in animal-based research.