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// Independent samples t-test in SPSS - checking normality assumption //

The main requirement of the independent samples t-test, next to the interval or ratio level for the test variable, is normal distribution of said test variable - for each of the two groups being compared.

Despite there being three ways to test for normal distribution, I) analytics tests like the Shapiro-Wilk-test or Kolmogorov-Smirnov-test, II) a histogram or III) a Q-Q-plot, I will only show the latter for the following reason:
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
0:15 Analytical tests (Shapiro-Wilk-Test, Kolmogorov-Smirnov test) - why not to use
0:39 Q-Q-Plot creation
1:05 Q-Q-Plot interpretation


If you have any questions or suggestions regarding the normal distribution testing for the independnt samples t-test, please use the comment function. Thumbs up or down to decide if you found the video helpful.
#statorials


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