Regression adjustment is one of the most common tools in biostatistics and health research. It is often treated as proof that a study has properly controlled for differences and moved closer to the truth. But what if regression adjustment is creating more confidence than validity? 

In this episode, we break down why regression models often fail to remove bias, how adding more covariates can sometimes make analysis worse, and why statistical control is much weaker than most people think. If you use or interpret regression results, this is a concept you cannot afford to ignore.

👉 Enjoyed the episode? Follow the show to get new episodes automatically.

If you found the content helpful, consider leaving a rating or review—it helps support the podcast.

For business and sponsorship inquiries, email us at:
📧 [email protected]

Youtube: https://www.youtube.com/@BJANALYTICS

Instagram: https://www.instagram.com/bjanalyticsconsulting/

Twitter/X: https://x.com/BJANALYTICS

Threads: https://www.threads.com/@bjanalyticsconsulting