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At least speaking as a academic (4th year PhD student), one of the challenges with researchers over reliance on NHST seems to be the apparent lack of a more com
by neuro_logical 9y ago
At least speaking as a academic (4th year PhD student), one of the challenges with researchers over reliance on NHST seems to be the apparent lack of a more compelling alternative. One candidate which is gaining traction is Bayes factors but there are challenges with this approach as well e.g. with suitable specification of priors. The best way forward will involve fundamentally restructuring how we educate incoming researchers because it will necessarily mean embracing uncertainty over dichotomization of results into yes/no. Andrew Gelman and John Carlin write elegantly about ways forward here: http://www.stat.columbia.edu/~gelman/research/published/jasa_signif_2.pdf http://www.stat.columbia.edu/~gelman/research/published/jasa...
- nonbel 9y agoThe alternative is to stop testing a default "nil" hypothesis and instead come up with various mathematical/computational models for the process that generated the data, then test that on future data. The model that makes the most precise and accurate predictions with the least assumptions/complexity should be used until a better one comes along. This is quite honestly just how science used to be done before NHST. It still is done this way in many areas of physics and engineering.