We have seen confounders before; it is a factor that associates with both exposure and outcome, thereby deceiving investigators of a causal relationship between the two.
For example, smoking is a confounder that misleads people to conclude that drinking can lead to lung cancer. In reality, smokers have a higher tendency to drink, and smokers have a higher tendency to get lung cancer. Until you stratify and find the impact of drinking on smokers and non-smokers, you are unlikely to figure out the error.
On the other hand, if the variable impact the outcome and not the exposure, it is an effect modification. A simple example is the immunisation status of an individual can impact the person’s susceptibility to getting the infection from the virus.
