# QQ plot result doesn't correspond to normality test [duplicate]

I am considering an ANOVA model for such data:

Student    Class  Points on exam   Day of studying
1           P1       12             Day 10
2           P3       23             Day 5
3           P3       8              Day 1
4           P2       36             Day 10
5           P1       10             Day 1
6           P2       86             Day 10
7           P1       13             Day 5
...


and such ANOVA model:

model <- aov(Points ~ Class + Day, data)
ggqqplot(residuals(model))


how can I check if this model satisfies the normality of distribution? (other than the Q-Q plot of the model?) My Q-Q plot of the model (above) seems to show normal distribution but when I perform Shapiro-Wilk( that considers only Points) the p-value comes out incredibly small... Therefore I don't know if I can assume the normality or not.

Shapiro-Wilk normality test

data:  Points
W = 0.92212, p-value = 9.97e-13

• "My Q-Q plot of the model (above) seems to show normal distribution". Does it?
– epp
May 22 at 6:46
• The only normal quantile plot of interest here is of the residuals. Is that what you are showing. A plot of the "model" could be many things. (I am not a routine R user but in any case if I understand correctly you don't show the syntax used to produce the plot. May 22 at 8:37
• @NickCox you are right! I updated the question May 22 at 9:01
• Thanks for the clarification. So, compare like with like: Shapiro-Wilk on the residuals will I guess reject overwhelmingly too, but that doesn't mean much. The real issue is whether these results point to a different analysis, which I doubt. You have Points and at least 3 classes and at least 3 days. Unless the distribution of Points is. conditional, on predictor, very skewed I doubt there is a strong case for doing anything different. The threads cited by @Tim are helpful here. May 22 at 9:50

Just on first look, this distribution looks very short tailed, as you can see it looks kind of like this simulation with a uniform distribution 