Be Biologist: Basic Knowledge for Biologist Which No One Told 5

Biological research increasingly relies on high-throughput and high-dimensional measurements, making experimental design and statistical analysis inseparable determinants of credibility. A frequent failure mode in early-stage research is treating statistics as an end-stage formality rather than a design principle, which inflates false positives, reduces power, and weakens reproducibility. This article presents a practical, design-to-analysis framework for novice experimental biologists. Key elements include rigorous control-group construction (negative, vehicle, sham, and positive controls), bias prevention through randomization and blinding, clear separation of technical versus biological replication, and proactive management of batch effects. Guidance is provided on sample-size planning and data quality control as prerequisites for valid inference, along with interpretation strategies that account for interaction terms and hierarchical or correlated data structures. For mean comparisons, the logic of multiple testing is formalized via family-wise error rate control, and decision rules are outlined for selecting post-hoc procedures aligned with the comparison objective (all-pairs versus control-based contrasts). Finally, a reproducible workflow using open-source R/Python tools in cloud notebook environments (e.g., Google Colab) is described, including how large language models can lower the barrier to script-based analysis without replacing statistical reasoning. Emphasis is placed on reporting effect sizes and confidence intervals to support biologically meaningful conclusions beyond statistical significance.