A Statistical Framework for Robust and Reproducible BioImage Analysis

Abstract

Bioimage analysis is a powerful tool for investigating complex biological processes, but its robustness depends on technical precision and rigorous experimental design. In particular, the use of appropriate controls and experimental repetition is critical for drawing meaningful conclusions. However, both are often used inadequately or overlooked, with "statistical significance" often prioritised, frequently obtained through the misuse or misinterpretation of statistical tests. In this study, we reanalyse publicly available image datasets to highlight the crucial role of robust experimental design in interpreting results. Our findings underscore the importance of focusing on effect sizes and biological relevance over arbitrary statistical thresholds. We also discuss the diminishing returns of increased data collection once statistical stability has been achieved. By refining control usage and emphasising effect sizes, this work aims to enhance the reproducibility and robustness of research findings. We provide open-access code to allow researchers to engage with the dataset, promoting better practices in experimental design and data interpretation.