Why is my data not normally distributed while I have an almost perfect QQ plot and histogram? I am doing a thesis and I am stuck with my statistics on time series data. And when I run a Shapiro test, or any test, it says:
Statistics=0.998, p=0.000
Sample does not look Gaussian (reject H0)

And that it is not normally distributed. But when I look to my QQ plot and histogram the data looks normally distributed. And even when it would not pass the test, it is strange that it results in p = 0.000.
I also have two other groups where the QQ plot and histogram looks way worse than this one, but the tests say those are normally distributed.
I am looking in my script, but I can’t find any mistakes so far. Is it likely that there is something wrong or is it very normal for this data to be nonparametric (even with this graphs)?
I am using Python btw, not R.
QQ plot:

Histogram:

PS. I also have a box plot:


No data will ever be perfectly normally distributed, so it is not surprising that your data are "not normal", and note that no test ever will say "data are normal"; the best any test can tell you is that according to the test statistic data do not deviate from what is expected under the normal distribution, but data may not be normal anyway (for example due to not being truly continuous, as no data are).
As data are never truly normal, true normality is not what is of interest in applied data analysis. What is really relevant is if you have deviations from normality that have the potential to mislead conclusions of methods that supposedly assume normality. But this depends on what you think you need normality for, i.e., what you want to do with the data after having tested normality. Chances are the data shown in your plots are fine for most things you'd like to do (there are no extreme outliers and no strong skewness, so theory based on normality will give very good approximations), but without knowing what you want to do based on normality, we can't know for sure. (Note that most analyses have further assumptions on top of normality, and sometimes problems with those are worse, particularly independence.)
