Timeline for What's wrong with Bonferroni adjustments?
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
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| Feb 14, 2018 at 4:51 | comment | added | Nakx | But isn't it more transparent and useful to report all the raw p-values in an article, so that readers can judge by themselves of their validity or choose which of the myriad of adjustment methods they want to use? | |
| Aug 11, 2016 at 12:55 | history | edited | amoeba | CC BY-SA 3.0 |
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| Aug 5, 2016 at 10:51 | comment | added | user83346 | But if I understand well, the FDR (false discovery rates) do not guarantee type I error control at a predetermined level ? (see also my answer to this question) | |
| Aug 5, 2016 at 10:02 | comment | added | Silverfish | I'm also concerned this answer seems to conflate methods of preserving familywise error rate with those for false discovery rate. It isn't a bad idea to be discussing both these things, but since they do different jobs I don't think they should be presented as equivalent | |
| Aug 5, 2016 at 10:00 | comment | added | Silverfish | Your method of calculating the 40% chance of false positive in ten tests is premised on your tests being independent events but with real data this is quite unlikely to be the case. I think that is at least worthy of comment. | |
| Oct 17, 2014 at 13:08 | comment | added | martino | There are several alternatives – Holm Bonferroni for example is simple and easy to understand. Why not give it a go. Let’s say you application is in gene expression or protein expression where you are testing possibly thousands of variables in an experiment then you FDR is typically used. | |
| Oct 17, 2014 at 12:47 | vote | accept | goro | ||
| Oct 17, 2014 at 13:59 | |||||
| Oct 17, 2014 at 12:46 | vote | accept | goro | ||
| Oct 17, 2014 at 12:47 | |||||
| Oct 17, 2014 at 12:27 | history | answered | martino | CC BY-SA 3.0 |