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Adjustment Explained — Design vs Analysis | Practical Guide to Research Identifying confounding is only the first step. The real challenge in research is controlling it — correctly. In Episode 6 of the Practical Guide to Research series at Handy Research, we explore adjustment: how researchers control confounding at both the design stage and the analysis stage. In this lecture, we discuss: • Why adjustment is necessary • Exchangeability and comparability • Design-stage control: randomization, restriction, matching • Analysis-stage control: stratification and regression • Propensity score methods — what they are and when to use them • Why adjustment must follow causal reasoning, not statistical reflex Adjustment is not about adding variables into a model automatically. It is about restoring comparability between exposure groups in a way that aligns with causal structure. Understanding this distinction is essential for interpreting observational studies, clinical research, database analyses, and even meta-analyses correctly. ⸻ 🎓 About the Series The Practical Guide to Research is a structured educational series designed to build research thinking step by step — from foundational epidemiologic concepts to advanced statistical analysis and evidence synthesis. This is not just theory. This is structured, practical research education. ⸻ 📌 Who This Is For • Medical students • Clinical researchers • Public health professionals • Epidemiology learners • Anyone who wants to interpret research critically ⸻ 🔔 Follow Handy Research Subscribe for structured, professor-level research education — from causal reasoning to advanced methodology. ⸻ 🔎 Keywords adjustment in research, control confounding, design vs analysis, propensity score explained, epidemiology methods, causal inference basics, regression adjustment, practical guide to research