Abstract
The modern biology toolbox continues to evolve, as cutting-edge molecular techniques complement some classic approaches and replace others. However, statistical literacy and experimental design remain critical to the success of any empirical research, regardless of which methods are used to collect data. This Perspective highlights common experimental design pitfalls and explains how to avoid them. We discuss principles of experimental design that are relevant for all biology research, along with special considerations for projects using -omics approaches. Established best practices for optimizing sample size, randomizing treatments, including positive and negative controls, and reducing noise (e.g., blocking and pooling) can empower researchers to conduct experiments that become useful contributions to the scientific record, even if they generate negative results. They also reduce the risk of introducing bias, drawing incorrect conclusions, or wasting effort and resources on experiments with low chances of success. Although experimental design strategies are often covered in undergraduate- and graduate-level courses and in textbooks, here we provide a succinct overview and highlight their relevance to modern biology research. This Perspective can be used in training of early-career scientists and as a refresher for seasoned scientists.