Timeline for Is multiple testing a problem because variables in data have not been controlled for? and there might be correlation between them?
Current License: CC BY-SA 4.0
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when toggle format | what | by | license | comment | |
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Nov 24, 2019 at 13:27 | answer | added | Peter Flom | timeline score: 0 | |
Nov 24, 2019 at 13:24 | history | reopened | Peter Flom | ||
Nov 23, 2019 at 23:16 | comment | added | Sal Mangiafico | The issue of there being correlation among your multiple independent variables (X1, X2, X3) is a separate issue from that of multiple testing. With either concern, it doesn't make either multiple regression or multiple simple regressions incorrect. It just requires some caution in interpretation. | |
Nov 23, 2019 at 18:25 | review | Reopen votes | |||
Nov 24, 2019 at 13:24 | |||||
Nov 23, 2019 at 18:08 | history | edited | Saina | CC BY-SA 4.0 |
reworded my question
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Nov 22, 2019 at 23:19 | history | closed |
Michael R. Chernick BruceET Djib2011 Peter Flom |
Needs details or clarity | |
Nov 22, 2019 at 23:19 | comment | added | Peter Flom | If you generate random noise data and do 10 hypothesis tests at p = 0.05, there will be far more than a 5% chance that at least one of the results will be siginficant. | |
Nov 22, 2019 at 7:18 | comment | added | BruceET | If you have $k = 5$ levels of the factor in a one-way ANOVA, you are correct that there are ${5 \choose 2} = 10$ possible comparisons among levels--provided the main F-test finds not all means equal. The 10th post hoc test is no more likely to be significant that the first, but if you do ten tests, each at the 5% level, you have more than a 5% chance of finding a difference somewhere among 10 tests. | |
Nov 22, 2019 at 2:00 | review | Close votes | |||
Nov 22, 2019 at 23:19 | |||||
Nov 22, 2019 at 1:35 | review | First posts | |||
Nov 22, 2019 at 1:41 | |||||
Nov 22, 2019 at 1:32 | history | asked | Saina | CC BY-SA 4.0 |