Confounding is an important concept in epidemiology, because, if present, it can cause an over- or under- estimate of the observed association between exposure and health outcome. The distortion introduced by a confounding factor can be large, and it can even change the apparent direction of an effect.

Does a confounder have to be significant?

It is irrelevant if the confounder itself is significant. If there is any reasonable chance that the response may change over time (and actually, there always is!), "time" should be considered in the model.

Why do confounding variables matter?

Why confounding variables matter

To ensure the internal validity of your research, you must account for confounding variables. ... Even if you correctly identify a cause-and-effect relationship, confounding variables can result in over- or underestimating the impact of your independent variable on your dependent variable.