How to check normal distribution | The normality assumption

See all my videos at:
www.tilestats.com/
1. Histogram
2. QQ plot (02:45)
3. Shapiro-Wilk (03:11)
4. An example of exponential distribution (08:40)
5. Type 1 and 2 errors in Shapiro-Wilk (10:49)
6. The normality assumption (14:49)
7. How to check the normality assumption (15:43)

[@dawitabathun9402]

great

[@Unaimend]

Thanks for the video

[@Leila0S]

Many thanks to him indeed
His videos come as a help in difficult times

[@manuelleitner3196]

Thank you for your video. In the case of n>30 and highly skewed data, would you prefer a non-parametric test over the option of bootstrap? e.g. in a scenario where you analyze group differences, would you use a Mann-Whitney U Test or an unpaired t-test with 10k bootstrap samples?

[@tilestats]

Hard to say because there are many types of skewed distributions. Anyway, in this video:
ruclips.net/video/mOzVwv9ob9Q/видео.html
I show that the MWU test has higher statistical power than the t-test for a log-normal distribution. I also tried permutation tests, such as the one shown in this video:
ruclips.net/video/v7u8lHgoWig/видео.html
and bootstrap confidence intervals (not shown in the video though) and they had a power between the MWU and the t-test. Thus for a log-normal distribution, MWU performs best. However, for other types of skewed distributions, you might get different results.

[@manuelleitner3196]

@@tilestats thank you very much for your fast reply!

[@tomkrechely4166]

Im waiting already to your next video. Btw, cant we use more robust tests when the data is highly skewed? And how much is highly skewed?

[@tilestats]

Yes, you can use more robust tests for highly skewed data. However, to define highly skewed is somewhat arbitrary. In the video below, I show that a non-parametric test has a higher statistical power for highly skewed data compared to a parametric test:
ruclips.net/video/mOzVwv9ob9Q/видео.html