Timeline for Frequentist vs bayesian and P(data | H0) vs P(H0 | data) giving same result
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Jul 21, 2021 at 12:00 | history | tweeted | twitter.com/StackStats/status/1417816655422533634 | ||
| Jul 21, 2021 at 10:00 | comment | added | Christian Hennig | The p-value may happen to be equal or approximately equal to a Bayesian probability that H0 is true given a certain prior, but still according to frequentist logic it is something essentially different. | |
| Jul 21, 2021 at 9:58 | comment | added | Christian Hennig | Note that in frequentist analysis the parameters and hypotheses are not random variables, so it's technically wrong to write $P(data|H0)$, because this suggests that it's a conditional probability based on the random variable H0 taking a certain value. It's more correct to write $P_{H0}(data)$, as this says that it's the unconditional probability of the data assuming that H0 is true. Consequently no frequentist analysis will give you a probability that H0 is true (the p-value is definitively not such a probability), because such a probability is not defined in frequentism. | |
| Jul 21, 2021 at 8:04 | vote | accept | jcp | ||
| Jul 21, 2021 at 7:32 | answer | added | Tim | timeline score: 1 | |
| Jul 21, 2021 at 0:00 | history | edited | jcp | CC BY-SA 4.0 |
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| Jul 20, 2021 at 23:17 | history | asked | jcp | CC BY-SA 4.0 |