I think that all of these arguments are pivoted off the same idea. Roughly, but not exactly(): (Co-)existence is necessary for correlation Correlation

I think that all of these arguments are pivoted off the same idea. Roughly, but not exactly(): (Co-)existence is necessary for correlation Correlation is necessary for causality Or maybe (E > Co > Ca). What this means is two-fold 1. It's valuable and common to note E and Co suggestively of Co and Ca respectively. If I *notice* E then I begin to hypothesize about Co because I know have evidence for its possibility if not yet evidence for Co directly 2. Being lazy/imprecise in speech or reasoning might cause someone to "skip a step" and claim something more powerful than what they actually have. With regard to (1), I think it's completely valuable to make these observations. At their heart, they're nothing more than broadcasting and contextualizing data* and that's an important function. So (2) is where all of the danger lies---properly contextualizing data and what it actually gives credence to. So how can we make sure that (2) occurs casually without precluding (1)? I think that articles like this one and all around the whole "correlation is not causation" tagline are great. They help to ensure people remember the size of the space between each step in E > Co > Ca. I think another powerful technique would be to demonstrate more arguments that make the jumps and highlight the properties which allow that to happen. Chris did a good job here demonstrating correlation—what it looks like when it exists or fails to exist—but much, much more can be said about the E > Co jump. The Co > Ca jump is far more complex. Worse, it's often obscured through opaque words like "randomized, controlled study" or "scientific method" which are actually quite far away from the mechanisms which allow that jump to be made—they're more like implementation details obscuring a great API. Demonstrating clear arguments here (and not "rain + wet grass" oversimplified ones) could be a great boon to public reasoning. What else can be done to reduce (2) without precluding (1)? (*): Really (linear) correlation is just one kind of relationship-of-interest between things. This is often pointed out when people talk about correlation in terms like "circular relationships have 0 linear correlation, oh no!" What I'd like to write instead might be E > M > C where M becomes "model building". Choosing to highlight linear correlation means that you're choosing a linear model. That might be perfect, or it might be wrong, but you still must choose it before you can structure your observations into evidence of some kind of causality. Generally, at this point, you would also want to begin developing covariates all in preparation for the C step.