Nonsense associations can arise when an exposure and an outcome of interest exhibit similar patterns of dependence. Confounding is present when potential outcomes are not independent of treatment. This talk will describe how confusion about these two phenomena results in shortcomings in popular methods in three areas: causal inference with multiple treatments and unmeasured confounding, causal and statistical inference with social network data, and spatial confounding. For each of these areas I will demonstrate the flaws in existing methods and describe new methods that were inspired by careful consideration of dependence and confounding.