What Researchers Should Check Before Trusting a Regression Model

In observational and clinical research, the validity of your conclusions often hinges on a single assumption: that your control group is meaningfully comparable to your exposure or intervention group. When this assumption fails, even technically correct analyses can produce misleading results. Assessing comparability is not a box-checking exercise. It requires deliberate evaluation of study design, data structure, and baseline characteristics, long before modeling begins.

In clinical research, few problems cause more hidden damage than confounding. It distorts treatment effects, creates false associations, and leads investigators to conclusions that are statistically valid, but scientifically wrong. What makes confounding especially dangerous is that it often remains invisible until long after the paper is published. Fortunately, you do not need advanced modeling to detect it. Many of the most powerful warning signs of confounding appear long before any...