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, there are times when both are inadequately applied or overlooked in favour of 'statistical significance', often derived from misused or misinterpreted statistical tests. In this Perspective, 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 Perspective offers guidance 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.
Marcotti et al. (2026) studied this question.