Type I and Type II Errors: The Inevitable Errors in Optimization Experiments

Re:Type I and Type II Errors: The Inevitable Errors in Optim... by avz on Thursday April 09, 2020 @04:04PM (#22826601)

To highlight the persistence of the confusion compare the confusion matrix in two wikipedia articles: [1] and [2].

[1] https://en.wikipedia.org/wiki/Confusion_matrix

[2] https://en.wikipedia.org/wiki/Pre-_and_post-test_probability...

(In case edits are made one way or the other: both articles display similar confusion matrices but disagree on the names of the off-diagonal elements. In [1] false positives are called "type I errors" and false negatives are called "type II errors". In [2] false positives are called "type II errors" and false negatives are called "type I errors".)

(Edit: layout)

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Re:Type I and Type II Errors: The Inevitable Errors in Optim... by ImaCake on Thursday April 09, 2020 @05:02PM (#22827157)

I hate the number naming system. I have no idea why anyone would find it preferable to use them for literally any reason. Specificity and Sensitivity already almost mean the right things in common language already, they are perfect words for this. The only use for these is to confuse people so they don't realise you have no idea what you are talking about.

I think some people struggle to grasp that these are actually strict mathematical concepts. But personally, that makes me feel comfortable with them. It is a lot easier to get a math concept consitently right than some of the fuzzier concepts in epidemiology.

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