The key quote from the SO post you link is the following, especially the second half:
When the sample size is small, even big departures from normality are not detected, and when your sample size is large, even the smallest deviation from normality will lead to a rejected null.
With normality tests (or any other goodness-of-fit test), there is always a continuum. If your data generating process is even slightly non-normal, then normality will be rejected if your $n$ is large enough. Look at that SO post again: even a $t$ distribution with 200 degrees of freedom will be rejected as non-normal given enough data - and that is about as normal as you can ever get, unless you are explicitly generating normal data.
Of course, it is no surprise that normality tests perform as expected if you have explicitly generated normally distributed data. Unfortunately, that doesn't tell you much about what happens with real data.
You seem to have more than 1500 data points. I'd say that you are well and truly beyond the threshold at which small deviations from non-normality are picked up.
The question isn't whether data are normally distributed. They never are. (Simply measuring accuracy means that real measured data are in the end always discrete, so they cannot be normal.) The question, if at all, is whether they are sufficiently normal for whatever you need normality for. And I'd argue that they typically are. For instance, ANOVA F tests are surprisingly robust against non-normally distributed residuals, since what is relevant is actually the normal distribution of the parameter estimates, which is certainly given once you have enough data that your normality tests start rejecting residuals.
Bottom line: think about why you need normality. And whether at all.