Different results for residual normal distribution between Jarque-Bera test and Q-Q Plot I am trying to test for normality of residuals using 2 different ways:

*

*Using Jarque-Bera test

*Q-Q Plot

I can see different results, for the JB test the value is 19.9553 with a probability of 0.00005. Thus, we can't reject the null hypotheses, and this concludes that there is a non-normal distribution of results.
On the other hand, when I plotted the same dataset using Q-Q graph, I could see a partially linear relation, which might point to a normal distribution. Given the size of observations is 62 and the regression model that was used is the OLS model.

Do you think I did something wrong in my assumption?
 A: Here are six samples, of sizes $10,10,10,1000,1000,1000$ and
from distributions standard (uniform, exponential, normal, uniform,
exponential, normal), respectively.
A normal Q-Q plot is shown for each sample, with the Shapiro-Wilk
P-value in the upper-left corner of each.
It is difficult to judge
normality by looking at only ten observations. The Q-Q plots are
generally useful for samples of a thousand. And, with 1000 observations, the S-W test often
gives helpful results. (The S-W normality test is widely used
and many statisticians consider it among the best, but I am not claiming it is best for your purposes.)

R code for three panels of the figure is shown below. (Elegance was
sacrificed for simplicity.)
par(mfrow=c(2,3))
set.seed(2021)

x = runif(10)
pv=shapiro.test(x)$p.val; pv
[1] 0.8485772
qqnorm(x, main="10 Unif"); qqline(x, col="blue")
 text(-1.1, .9, round(pv,5))

...

z = rnorm(10)
pv = shapiro.test(z)$p.val; pv
[1] 0.08000124
qqnorm(x, main="10 Norm"); qqline(x, col="blue")
 text(-1.1, .9, round(pv,5))

u = runif(1000)
pv = shapiro.test(u)$p.val; pv
[1] 2.473175e-17;  if(pv < .0001) pv = 0
qqnorm(u, main="1000 Unif"); qqline(x, col="blue")
 text(-3, .82, round(pv,5))

 ...

par(mfrow=c(1,1))

Note: There are several comprehensive discussions about
best methods to judge normality on this site. You might
start by looking at some of the pages linked in the
margin as 'Relevant', then try the search facility at
the top of the page. And some of my colleagues may
post comments with favorite links.
