this post was submitted on 11 Jun 2026
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The example doesn't only highlight the difference between correlation and causation, it's just one of the ways naive data fetishism is ineffective science. There is no science without experimentation, and there is no experimentation without theoretical frameworks.
Let's come back to the core of the discussion instead of getting stuck in a what can be other discussions. Minds know things through testing. If reality was inherently inconsistent then there would be nothing to know. So far reality doesn't seem to be inconsistent therefore there is testable knowledge.
If the goal is human wellbeing and the wellbeing of the environment then we can formulate a hypothesis on how to achieve that and methods to quantify and measure human wellbeing and environmental impact. Then we can employ said hypothesis while maintaining a control and measure the outcome to see what effects it had, if the hypothesis is accurate. Without a way to measure there's no way to know if a hypothesis is good or bad or neutral towards the goal. We can test political philosophies, of your argument is that we can not then you're inferring there are no means through which we can determine if it's a good idea or not.
Very well said, much more clear than me!
Either we can test and measure or we cannot. If we can't test a concept then there are no means through which to determine it is a beneficial, detrimental, or neutral concept. If the goal is increasing human and environmental wellbeing we can definitely create measures to ensure an idea when employed is getting us closer to said goal, taking us further away, or it has a neutral effect. Once again if there is no means through which to test there is necessarily no way to determine if the concept (hypothesis) is correct or not.