Clarity over complexity: Statistical reporting that resonates

ABSTRACT Effective statistical reporting is essential for the credibility, reproducibility, and advancement of scientific research. Despite existing guidelines, many researchers—especially early career researchers—feel underprepared to handle increasingly complex statistical methods. This gap is notable in fields like fisheries science, where advanced quantitative tools are often required for critical decision making. Unclear statistical reporting—whether due to selective reporting, insufficient sample sizes, or inadequate model understanding—undermines the integrity and use of research findings. This article reviews common issues in statistical reporting and offers practical recommendations for clarity, transparency, and objectivity. We organize topics into general concerns, common issues, and small errors to provide a relative magnitude to the nature of the issue. Topics include selecting appropriate models, avoiding significance bias, addressing challenges with small sample sizes, and ensuring reproducibility, among others. We advocate for clear documentation of methods, effective use of visuals, and incorporation of supplemental materials, such as data and code, to facilitate understanding and replication. Rather than prescribing absolutes, we encourage researchers to embrace practices that prioritize clarity and reader comprehension. By adopting these recommended practices, scientists can ensure their work is not only accessible but also capable of advancing knowledge and informing policy—often the primary goal of scientific reporting.