1. Study Design: Independence assumptions are often required when designing studies. For example, in a case-control study, the exposure status should be independent of the outcome in the control group to avoid bias. 2. Statistical Analysis: Many statistical tests, such as the Chi-square test, assume independence between variables. Violating this assumption can lead to incorrect conclusions. 3. Causal Inference: Establishing independence is crucial for identifying causal relationships. If two variables are not independent, one might influence the other, complicating the determination of causality.
