When I think about the difference between "relationships" and "cause and effect," especially in the context of analyzing nursing home data, it really hits home how important this is for real-world decision-making. Working with stats, it's so tempting to claim that, for example, upping staff hours causes better resident outcomes. But honestly, just noticing that two things happen at the same time doesn't mean one makes the other happen. Siegel (2016) really drives this point in Chapters 11 and 12, reminding us that tools like correlation and regression only show us patternsthey don't prove one variable is behind the changes in another. In my own work or hypothetical projects with nursing homes, I've seen how factors like staff turnover, funding, and resident health ratings often move together. But Siegel (2016) makes it clear that unless I have the luxury of running a real experiment (which, let's be honest, is almost impossible in nursing home environments), I can't confidently say that increasing funding will directly lead to improved resident health. There are just too many other thingsstaff morale, regulatory changes, local health trendsthat could be impacting both at the same time (Siegel, 2016, Chapter 11). Siegel's chapters also taught me to be super careful with time-series data, such as when resident satisfaction scores seem to rise right after a new policy rolls out. My gut might say, "That new policy worked!" but, as Siegel (2016) explains in Chapters 13 and 14