How to Choose Your Statistical Test Before You Run It: A Decision‐Based Guide to Statistical Analysis in Aquatic Sciences for Young Scientists

Abstract Statistical analysis is a cornerstone of scientific research, yet young researchers in aquatic sciences often struggle when analyses begin without a clearly defined research question or a solid understanding of the data structure. This disconnect between data collection and analytical decision‐making recurrently drives unreliable findings, manuscript rejection, and reproducibility failures. Here, we present a practical, decision‐based guide to statistical analysis structured around three foundational layers: the question layer, which addresses how to formulate a precise, answerable research question before any data are examined and introduces the distinction between diagnostic and analytical exploration; the data structure layer, which focuses on understanding the existing sampling design, data structure, variable types, and applying these exploratory principles in practice; and the execution layer, which guides method selection, assumption checking, result interpretation, and transparent reporting. These layers are anchored by a central decision flowchart supporting iterative, reproducible workflows. We also discuss the role of artificial intelligence (AI)‐assisted tools as analytical companions, used in accordance with institutional and journal policies, and the importance of open science practices, including code and data deposition in public repositories. This guide offers young aquatic scientists a practical foundation for navigating the full cycle from research question to reproducible output.