Testing For Normality - Clearly Explained
In this video, I will provide a clear overview of normality testing data. Testing for normality is an important procedure to determine if your data has been sampled from a normal (Gaussian) distribution.
There are two main ways that are commonly used to deduce whether data have been sampled from a normal distribution: analysis of graphs (eg, Q-Q plots and frequency distributions) and performing normality tests (eg, Shapiro-Wilk test).
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![Normality test [Simply Explained]](/img/d.gif)
Пікірлер: 85
Once again you have found a way to simply describe something that can be difficult to comprehend. Your explanations and videos are truly first rate.
Thank you so much for such an informative and useful guide. I write my bachelor thesis and try to find out if my data is normally distributed. Thanks to your clear explanations, now I know exactly how to test it!!👍🏼
fantastic explanation. the entire normality confusion is cleared now. i wish this channel comes up with more statistical chapters.
I find it the best video currently available on KZread👍🏼👍🏼👍🏼
simply put, you are great. keep up the outstanding job man
Great explanation, thank you so much!!
When p-value is bigger than 0.05 we do not reject the alternative hypothesis. The only thing we are observing is whether or not we reject the null hypothesis, therefore only thing we can reject is the null hypothesis if p-value is below our significance level. Otherwise great vid.
Good video saving this for a reference point to anyone looking into BI Data Analysts prep kit I'm making
Thank you very for your fantastic explanation!
Nice vid! keep up the good work.
absolutely fantastic. really interesting point about power
Tremendous explanation. Thanks.
That really helps.. thank you so much
Very clear...thank you!
Very helpful. Thank you
Thank you very much for this explanation !!!
Fantastic explanation!
thank you, it's easy to understand
Wonderfully explained
This is nice, short and knackig :). Thanx!