When variables are not independent, it can lead to:
1. Confounding: A confounder is a variable that is associated with both the exposure and the outcome, potentially distorting the true relationship between them. For instance, age might confound the relationship between physical activity and heart disease. 2. Bias: Non-independence can introduce bias in study results, particularly if the study design does not account for it. This can lead to over- or underestimating the association between variables. 3. Interaction: Interaction occurs when the effect of one variable on an outcome depends on the level of another variable. Recognizing and adjusting for interaction is crucial for accurate interpretation.
