Re:What a debate about p-values shows about science
by sideshowb on Tuesday October 03, 2017 @09:24AM
(#15392199)
Give them a break. It's a pop science article. Such articles can describe 1.5 concepts at most, so having used that budget on p-values, it's not available for Bayes, so explaining the concept from a frequentist perspective is reasonable.
Also Bayesian analysis (as a mainstream stats technique) is relatively new as it has only become mainstream with the advent of cheap computing power.
Re:What a debate about p-values shows about science
by cwyers on Tuesday October 03, 2017 @04:13PM
(#15395737)
Can I criticize the preprint from Lakens and his collaborators, which says the same thing as this "pop science" article?
Re:What a debate about p-values shows about science
by sideshowb on Tuesday October 03, 2017 @06:12PM
(#15396661)
Oh alright go on then ;)
Re:What a debate about p-values shows about science
by vinchuco on Tuesday October 03, 2017 @04:54AM
(#15391077)
Naive question: Is there a good technique for a bot that detects factually wrong statements?
Re:What a debate about p-values shows about science
by 09094920394314 on Tuesday October 03, 2017 @05:01AM
(#15391106)
Sure, just give me definition of factually wrong and I'build it for you:)
Less snarky: not really
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Re:What a debate about p-values shows about science
by sideshowb on Tuesday October 03, 2017 @09:21AM
(#15392183)
Ahem. Liberal here working on such a system (in concept anyway).
Re:What a debate about p-values shows about science
by 201709User on Tuesday October 03, 2017 @10:36AM
(#15392673)
Then machine learning it is.
> Yes, you’d want a very low p-value in a study that claims mental telepathy is possible. But do you need such stringent criteria for a well-worn idea? The high standards could impede young PhDs with low budgets.
Watching someone try to be Bayesian without using Bayesian techniques is painful.
> There are also new, advanced statistical techniques — like Bayesian analysis — that, in some ways, more directly evaluate a study’s outcome.
Almost as painful as watching Bayesian analysis described as "new."