Table 2 fallacy and stepwise regression

Link: https://towardsdatascience.com/table-2-fallacy-and-stepwise-regression-smoking-research-on-covid-19-hits-some-familiar-stumbling-615cf2e28c82

Excerpt:

One problem may be the way we teach statistics to data scientists and public health professionals. Multivariable regression is often mistaken for a silver bullet that magically controls away confounding for all variables at once, as long as no confounder is left out. This is what statisticians call the “Table 2 fallacy,” because the adjusted effect sizes in a multivariable model are so often reported in Table 2. Many medical professionals learn to read research articles critically for understanding without ever having been introduced to the Table 2 fallacy.

Confounding is often taught as a purely mathematical concept, but that misses the point. Throwing a large set of variously interrelated variables into a big stepwise regression model might be expected to work, if all you know about confounding is that you should “never leave a confounder out” of your analysis.

Author(s): Savanna Reid

Publication Date: 14 February 2021

Publication Site: towards data science