Timeline for Boxplot equivalent for heavy-tailed distributions?
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
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| Jul 3, 2013 at 11:14 | comment | added | Nick Cox | I offered that only as an example of people suggesting that transformations help in visualizing highly skewed data. There are many, many others. | |
| Jul 3, 2013 at 11:08 | comment | added | Quartz | Thanks for the nice link. That might seem related to this case but differs crucially. Here the issue is avoiding heavy tails to be treated as outliers, while there discussion is about "real" outliers and skewness. | |
| Jul 3, 2013 at 10:54 | comment | added | Nick Cox | I guess I need to stop here to let others judge, but my view is not eccentric. Transformation is discussed as one possibility at e.g. stats.stackexchange.com/questions/13086/… I suggest that you answer or comment there to explain why that advice is unsound. | |
| Jul 3, 2013 at 10:53 | history | edited | Quartz | CC BY-SA 3.0 |
added 13 characters in body
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| Jul 3, 2013 at 10:48 | comment | added | Quartz | So what? That has nothing to do with the case being discussed, which again is about heavy tails. | |
| Jul 3, 2013 at 10:43 | comment | added | Nick Cox | I disagree. I transform highly skewed data all the time and my experience is that this is far more than a question of aesthetics. It often works. An anonymous statistician wrote some time ago that the lognormal is more normal than the normal. He/she was being a little facetious but there's an important truth there too. (Not that many other distributions might not be better fits.) | |
| Jul 3, 2013 at 10:41 | comment | added | Quartz | Of course transformations in general can help, just not in the case discussed, where they'd hide an important feature of the data for aesthetic purposes. | |
| Jul 3, 2013 at 10:33 | comment | added | Nick Cox | Transformations often help: that's my bottom line. A statistical person who hasn't learned that many things look clearer on logarithmic scale (especially) is missing out seriously on the one of the oldest and most effective tricks there is. You seemed to be denying that; I hope I misread you. | |
| Jul 3, 2013 at 10:25 | comment | added | Quartz | To what difficulties in understanding skewed data are you referring to? Those with IQR-dependent whiskers? That's a problem even with light tails. And aren't we talking about heavy tails, independently of skewness? Transformations lightening tails surely give more regular boxplots, but add an interpretation layer, trading understanding for comfort. But one can call that a feature if he likes. | |
| Jul 3, 2013 at 9:37 | comment | added | Nick Cox | The last sentence is too unqualified to pass without comment. Transformation is not a panacea, but not transforming highly skewed data does not make any easier to understand. If the data are all positive, you can at least try using root, logarithmic or reciprocal scale. If it really doesn't help, then back off. | |
| Jul 3, 2013 at 9:22 | history | answered | Quartz | CC BY-SA 3.0 |