Extend or constructively challenge this post: (one or the other, do not do both):Implications of Misinterpreting Correlation as Causation Correlation analyses, such as bivariate correlation, are useful for investigating links between two variables in quantitative business research, but they do not prove causation. When researchers or practitioners misinterpret correlation as causation, it can mislead professional practice, leading to ineffective or harmful decisions. In my doctoral research on caregiver turnover, for example, a link between flexible scheduling and higher caregiver retention rates may simply indicate that agencies with better retention also offer flexible scheduling. When a leader presumes causation in the absence of experimental evidence, they may over-invest in scheduling while neglecting recognition, training and other important factors. Thus, misunderstanding correlation can lead to the misallocation of resources, erroneous implementation of strategies, and missing out on addressing the true causes of turnover (Rohrer, 2018). Weak Internal Validity of Correlation The outcomes of bivariate correlation analyses are viewed as weak in internal validity because they do not account for confounders, time order, and alternative explanations. Correlation shows that two variables are moving together, but it does not imply whether one causes the other. For example, caregiver recognition and turnover may be correlated, but this correlation may result from a third v