Timeline for How to judge if a datapoint deviates substantially from the norm
Current License: CC BY-SA 3.0
12 events
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| Jul 6, 2013 at 21:23 | history | edited | Scortchi♦ | CC BY-SA 3.0 |
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| May 24, 2012 at 18:24 | comment | added | IrishStat | @Andreas Statistical tests are done on averages/expectations. Your rule of thumb is a little bit too loose and can easily be tightened up by Michael Chernick's commentary and Gung's reflections. It is time that statistical rigor be incorporated rather than ignored. | |
| May 24, 2012 at 11:28 | answer | added | Chris Beeley | timeline score: 3 | |
| May 22, 2012 at 23:14 | history | tweeted | twitter.com/#!/StackStats/status/205074275592450048 | ||
| May 22, 2012 at 22:52 | comment | added | gung - Reinstate Monica | I think you need to have a validated time series model for the data prior to the outlier as @MichaelChernick notes. If it's really true that there are no time-based variations, then your final model will be a flat line, although that's hard for me to believe. Furthermore, if what you have is the entire population then statistical testing, or even calling any observation an 'outlier', makes no sense. If that is your population, then that is your population--that's the whole of the story; statements about 'significance' or 'outliers' are unintelligible. | |
| May 22, 2012 at 20:04 | history | edited | whuber♦ |
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| May 22, 2012 at 19:22 | comment | added | Andreas | I am not good at this sort of thing - but my thinking is that statistical tests is done on averages... So the question is wether any number belongs to the same distribution as the other numbers in the sample... But the tails of the distribution are in priciple indefinitely long. So any extreme number can pop up... You have to decide on a 'rule of thumb' I think. E.g. the boxplot rule where an outlier is a number more than 1.5 times +/- the interquatile range. | |
| May 22, 2012 at 18:13 | comment | added | benxyzzy | Thankyou, you've clarified a lot. I wonder though if the time element of this is overstated, therefore making the time series analysis aspect a red-herring? I don't think our data varies significantly as a function of time (that is, there aren't meaningful time-based variations to model). If this sounds hard to believe, let us for the sake of argument replace time on the x-axis with another, non-temporal variable. Let us also assume it is the entire population and not a sample. I need to a) identify a grouping or pattern in this population, and b) identify those that deviate significantly. | |
| May 22, 2012 at 18:13 | answer | added | Michael R. Chernick | timeline score: 10 | |
| May 22, 2012 at 17:45 | comment | added | gung - Reinstate Monica | In your second to last paragraph, according to standard hypothesis testing logic, you would want to assume a null hypothesis that the suspect datapoint is not an outlier, but is a product of the same underlying data generating process. Note that this requires a model of the DGP, and that moreover this is a big, & common, topic w/i time series analysis. I will let others on CV, who have more expertise in this area than I do, answer your question more fully from here. | |
| May 22, 2012 at 17:39 | history | edited | gung - Reinstate Monica |
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| May 22, 2012 at 17:37 | history | asked | benxyzzy | CC BY-SA 3.0 |