http://imaging.mrc-cbu.cam.ac.uk/statswiki/FAQ/Simon?action=AttachFile&do=view&target=kurtosis.pdf
Source:
Kim, H. Y. (2013). Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorative dentistry & endodontics, 38(1), 52-54.
1. For small samples (n < 50), if absolute z-scores for
either skewness or kurtosis are larger than 1.96, which
corresponds with a alpha level 0.05, then reject the
null hypothesis and conclude the distribution of the
sample is non-normal.
2. For medium-sized samples (50 < n < 300), reject the
null hypothesis at absolute z-value over 3.29, which
corresponds with a alpha level 0.05, and conclude the
distribution of the sample is non-normal.
3. For sample sizes greater than 300, depend on the
histograms and the absolute values of skewness
and kurtosis without considering z-values. Either
an absolute skew value larger than 2 or an absolute
kurtosis (proper) larger than 7 may be used as reference
values for determining substantial non-normality.
Source:
Brown, J. D. (1997). Skewness and kurtosis. Shiken: JALT testing & evaluation SIG.
source : https://www.medcalc.org/manual/testsfornormaldistribution.php
Why we need to do log transformation ?
https://www.youtube.com/watch?v=LCDiQxB5S84
https://www.youtube.com/watch?v=57An8xx3WjA
https://www.youtube.com/watch?v=w72mHLg4TnE
Recommended ways to interpret log transform in linear regression analysis:
https://stats.idre.ucla.edu/sas/faq/how-can-i-interpret-log-transformed-variables-in-terms-of-percent-change-in-linear-regression/
An Update and Extension to SEM Guidelines for Administrative and Social Science Research By: David Gefen
https://www.researchgate.net/post/How_do_you_transform_a_non-normal_set_of_data_into_a_normal_distribution
