Timeline for Tests for consistent measurements and outliers
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
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| May 10, 2012 at 22:27 | comment | added | Michael R. Chernick | Dixon's test is robust in small samples and has historically been used to see if there is one outlier in a set of three. Multiple testing can always be a problem. | |
| May 16, 2011 at 21:56 | comment | added | Nick Sabbe | @Aniko: I'm not even going there :-) I guess if there are 3 biological replicates with each 20 technical ones, the impact of multiple (3) testing will not be insurmountable (even a FWER correction only implies multiplying p-values by 3, so really strong deviations should get picked up easily). But you're right of course: @Leo did ask for 20 biological replicas :-( | |
| May 16, 2011 at 21:08 | comment | added | Aniko | The problem with doing a separate outlier detection for each replica is multiple testing, i.e. with enough tests you start getting false positives. | |
| May 16, 2011 at 21:05 | comment | added | Nick Sabbe | From what I quickly gather from The R outliers package description, I tend to say you may be able to say something about 20 observations, yes. But don't quote me on that. | |
| May 16, 2011 at 20:38 | comment | added | Leo | Ok, assume I have 20 biological replicas with 20 measurements in each, and I'm asking the same questions. Can I work at the level of 20 averaged measurements corresponding to 20 replicas as if this was my original sample? Or is it better to stay at the level of 400 measurements? | |
| May 16, 2011 at 20:12 | history | answered | Nick Sabbe | CC BY-SA 3.0 |