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You can never state the full nature of your experiment. Even the simplest experiment under the most controlled conditions has a bunch of unknown unknowns - you can never be certain that you didn't get a bad batch of reagents or a bit flip in your data or just royally screwed something up. Unless you're omniscient, there are always unknown unknowns lurking somewhere in your method.

In the case of your survey, you can't really state anything about the students you sampled with absolute confidence. You might have been asking a question about something that a lot of people are inclined to lie about. You might have been subconsciously biased and subtly influenced people towards the "right" answer. Slight inconsistencies in the wording of the question might radically alter how people respond. The student you tasked with conducting the interviews might have just stayed in bed and fabricated the data.

Bayesianism gives us an incredibly powerful framework for reasoning about this innate and unavoidable fog of uncertainty; frequentism largely pretends that it doesn't exist. The ongoing replication crisis shows why this is not merely pedantry, but the single most urgent issue in science.



the replication crisis is more about poor experiment design, low-n studies, and cherry picking results.

and that's not to say cherry picking is bad. lets say you set up an n=5 experiment and you drop your instrument on the floor on experiment #4. please cherry pick that one away.


>frequentism largely pretends that it doesn't exist. The ongoing replication crisis shows why this is not merely pedantry, but the single most urgent issue in science.

If you mean that Frequentist methods have no way of dealing with parameter uncertainty then your statement is false.

If you mean that some people who use Frequentist methods don't deal with parameter uncertainty then it may sometimes be the case.




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