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“He had acquired his belief not by honestly earning it in patient investigation, but by stifling his doubts. And although in the end he may have felt so sure about it that he could not think otherwise, yet inasmuch as he had knowingly and willingly worked himself into that frame of mind, he must be held responsible for it.”
https://statmodeling.stat.columbia.edu/2024/04/10/acq/
Statistical Modeling, Causal Inference, and Social Science 04/10/2024
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There is no golden path to discovery. One of my problems with all the focus on p-hacking, preregistration, harking, etc. is that I fear that it is giving the impression that all will be fine if researchers just avoid “questionable research practices.” And that ain’t the case.
https://statmodeling.stat.columbia.edu/2024/04/04/gold/
Statistical Modeling, Causal Inference, and Social Science 04/04/2024
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“Randomization in such studies is arguably a negative, in practice, in that it gives apparently ironclad causal identification (not really, given the ultimate goal of generalization), which just gives researchers and outsiders a greater level of overconfidence in the claims.”
https://statmodeling.stat.columbia.edu/2024/03/31/rand/
Statistical Modeling, Causal Inference, and Social Science 03/31/2024