Abstract
Bioimage analysis is a powerful tool for investigating complex biological processes, but its robustness depends on both technical precision and rigorous experimental design. In particular, the use of appropriate controls is critical for drawing meaningful biological conclusions. However, controls are often inadequately applied or overlooked in favour of achieving statistical significance, frequently obtained through the misuse or misinterpretation of statistical tests. In this study, we reanalyse a publicly available image dataset to highlight the crucial role of controls in interpreting experimental outcomes. 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, advocating for more efficient experimental designs. 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.
Competing Interest Statement
The authors have declared no competing interest.
