Timeline for Tests of normality - qq and Shapiro-Wilk
Current License: CC BY-SA 4.0
14 events
| when toggle format | what | by | license | comment | |
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| Jul 5, 2021 at 17:37 | answer | added | Peter Flom | timeline score: 1 | |
| Jul 5, 2021 at 17:13 | history | edited | kjetil b halvorsen♦ | CC BY-SA 4.0 |
added 8 characters in body
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| Jun 22, 2019 at 20:02 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Jan 23, 2019 at 9:00 | comment | added | Cairan Van Rooyen | I assume that I need to ensure that the data is normally distributed to employ an ANOVA in the first instance. Is this the data in each sample group or the aggregate data? And how do i check/test for normality? | |
| Jan 23, 2019 at 8:52 | comment | added | LuckyPal | @Glen_b I've read your answer in another thread regarding CLT in ANOVA. Makes sense to me, good to know! | |
| Jan 23, 2019 at 2:39 | comment | added | Glen_b | @LuckyPal The CLT alone doesn't get you there because the ANOVA test statistic isn't an externally standardized sample mean. You need more than the CLT because you need to deal with the denominator in the F. With the addition of another theorem, in the limit $n\to\infty$ it will give a foundation for getting correct type I error rates, but the CLT (etc) doesn't fix the (potentially substantial) issue with low efficiency (low power near H0) | |
| Jan 23, 2019 at 2:36 | comment | added | Glen_b | @Cairan 1. You can't "establish normality" ... only that your sample was too small to detect the non-normality you are sure to have. 2. for testing the normality assumption of some procedure, a number of relevant points are here | |
| Jan 22, 2019 at 18:23 | comment | added | BruceET | There are two issues here. The main one is that you should not expect the aggregate data to be normal even if the data have normal errors as specified in a linear model. I illustrate this for one simple case in my Answer. [Second, even if there is a slight departure from required normality, results of analysis such as a t test, ANOVA, or linear regression may be useful.] | |
| Jan 22, 2019 at 18:04 | answer | added | BruceET | timeline score: 2 | |
| Jan 22, 2019 at 16:59 | answer | added | LuckyPal | timeline score: 0 | |
| Jan 22, 2019 at 16:38 | comment | added | Cairan Van Rooyen | The total N=265 observations (participants) | |
| Jan 22, 2019 at 16:17 | comment | added | Cairan Van Rooyen | I am running lots of individual ANOVA's based on different groups. One typical set of groups: N=55,65,69,40,24,7 and 5. | |
| Jan 22, 2019 at 15:44 | comment | added | LuckyPal | What is your sample size per group? Assumption of ANOVA is not normality of the data but normality of the residuals (see stats.stackexchange.com/questions/6350/…). Further, with large N, Shapiro-Wilk tests tends to detect already quite small deviations from normal distribution. For large N, Central Limit Theorem provides a nice foundation to use ANOVA | |
| Jan 22, 2019 at 15:31 | history | asked | Cairan Van Rooyen | CC BY-SA 4.0 |