“On ‘Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science’, 2019”, 2019-09-17 (; backlinks; similar):
First, the title, which makes an excellent point. It can be valuable to think about measurement, comparison, and variation, even if commonly-used statistical methods can mislead.
This reminds me of the idea in decision analysis that the most important thing is not the solution of the decision tree but rather what you decide to put in the tree in the first place, or even, stepping back, what are your goals. The idea is that the threat of decision analysis is more powerful than its execution (as Chrissy Hesse might say): the decision-analytic thinking pushes you to think about costs and uncertainties and alternatives and opportunity costs, and that’s all valuable even if you never get around to performing the formal analysis. Similarly, I take Tong’s point that statistical thinking motivates you to consider design, data quality, bias, variance, conditioning, causal inference, and other concerns that will be relevant, whether or not they all go into a formal analysis.
That said, I have one concern, which is that “the threat is more powerful than the execution” only works if the threat is plausible. If you rule out the possibility of the execution, then the threat is empty. Similarly, while I understand the appeal of “Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science”, I think this might be good static advice, applicable right now, but not good dynamic advice: if we do away with statistical inference entirely (except in the very rare cases when no external assumptions are required to perform statistical modeling), then there may be less of a sense of the need for statistical thinking.
Overall, though, I agree with Tong’s message, and I think everybody should read his article.