One sample t-test in SPSS - checking normality assumption

// One Sample t-Test: Checking Normal Distribution //
The main requirement of the one sample t-test, next to the interval or ratio level for the test variable, is normal distribution of said test variable.
There are three ways to test for normal distribution:
I. Shapiro-Wilk-Test
II. Histogram
III. Q-Q-Plot
As with all analytical tests, large samples have more power and will “find” significant deviations
from normal distribution, even if those deviations are negligible. Therefore, caution is advised when blindly trusting a p-value.
Please refer, among many other publications, to Lantz, B. (2013). The large sample size fallacy. Scandinavian journal of caring sciences, 27(2), 487-492.
Eventually, put emphasis on the plots, mainly a histogram or a q-q-plot. I prefer the latter since one can manipulate the histogram with a proper "bin width".
Final note: a z-standardization before plotting a histogram or q-q-plot is optional. You will only see a slightly different histogram (reminder of the bin width) with less cliffs on the inside. The q-q-plot is not affected, hence my advise to use this as a test for normal distribution.
⏰ Timestamps:
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0:00 Introduction and overview
0:14 Possible testing
0:36 Q-Q-Plot
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