Timeline for Why is Pearson parametric and Spearman non-parametric
Current License: CC BY-SA 3.0
13 events
| when toggle format | what | by | license | comment | |
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| Dec 9, 2016 at 3:49 | answer | added | Julian Keenlyside | timeline score: 1 | |
| Mar 21, 2015 at 0:05 | comment | added | whuber♦ | Since it hasn't seem to have been clearly said yet, I would like to emphasize that no statistic is "parametric." That's like saying numbers are tasty: the adjective simply does not apply to the noun. Statistical models can be parametric (as indicated by the Wikipedia quotation), as well as the tests and procedures that are based on them. The Spearman and Pearson statistics can be used in both parametric and non-parametric settings. More on this at stats.stackexchange.com/questions/67204. What makes a model parametric is its state space. | |
| Mar 18, 2015 at 0:32 | history | tweeted | twitter.com/#!/StackStats/status/577991032358510592 | ||
| Mar 17, 2015 at 19:10 | answer | added | Aksakal | timeline score: 3 | |
| Mar 17, 2015 at 13:16 | answer | added | Hong Ooi | timeline score: 17 | |
| Mar 17, 2015 at 11:38 | comment | added | Nick Cox | @RichardHardy Thanks, but I have a horrible suspicion that this must be explained at length already. A conscientious search for duplicates should precede any attempt to write it up properly. | |
| Mar 17, 2015 at 11:37 | comment | added | Nick Cox | The point about normality of distribution only really bites when you want to do significance tests with correlation. If you use correlations only as descriptive measures, non-normality need not be a barrier to using correlations. Correlations can even be a little useful with two binary variables so long as both do vary. You still need to watch out for the effects of outliers, etc., etc. | |
| Mar 17, 2015 at 11:37 | comment | added | Richard Hardy | @NickCox, why don't you post that as an answer. | |
| Mar 17, 2015 at 11:33 | history | edited | Nick Cox | CC BY-SA 3.0 |
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| Mar 17, 2015 at 11:28 | comment | added | Nick Cox | Perhaps the simplest positive resolution of the question is this: yes, Spearman's correlation is a parameter to be estimated quantifying strength of a relationship and so resembles Pearson (at root, it's the same idea, as you point out); but no, Spearman's correlation is not a parameter that features in a distribution, whereas Pearson's is a parameter in a bivariate normal distribution (a historic but now downplayed interpretation of what you are doing when you do correlation). It's a fine distinction, to be understood by seeing that the word "parameter" has multiple senses. | |
| Mar 17, 2015 at 11:28 | comment | added | Nick Cox | It's a good question and there is an awful lot of misinformation out there. For example, the equation of parametric tests and assuming normal distributions is unfortunately a frequent confusion, whereby many textbook writers, course teachers and internet posters just copy from others who are as or more confused. | |
| Mar 17, 2015 at 11:23 | history | edited | Nick Cox | CC BY-SA 3.0 |
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| Mar 17, 2015 at 11:18 | history | asked | user2740 | CC BY-SA 3.0 |