Testing For Normality - Clearly Explained

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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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Software (Microsoft PowerPoint 365 ProPlus)

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[texaspolygraph]

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.

[TatianaMiloserdova-tx]

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!!👍🏼

[michalmokros]

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.

[AleksasViazovskis]

What a fantastic explanation, thank you so much, Steven!

[Plinktitioner]

Thank you for taking the time to publish such a helpful video. Much obliged.

[cvino]

Good video saving this for a reference point to anyone looking into BI Data Analysts prep kit I'm making

[MedDeviceJesus]

Very good video. Helped my staff to understand clearly, thank you!💪💪

[sayantan.mukherjee]

fantastic explanation. the entire normality confusion is cleared now. i wish this channel comes up with more statistical chapters.
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[michellecamacho]

Great explanation it helped me a lot with my data interpretation, thank you so much . Parting from here, would be great to have something like how to chose the proper statistical analysis for the data we are interpreting. It is yet very confusing

[elmakkiamiri]

simply put, you are great. keep up the outstanding job man

[LayneSadler]

absolutely fantastic. really interesting point about power 8:40

[pascalsigel]

note: if p>0.05 you not accept the null hipothesis, just fails to reject it. it is not the same.

[alirezasadeghi]

I find it the best video currently available on YouTube👍🏼👍🏼👍🏼

[sunnyyoda]

Your videos are absolutely amazing!! How do you prep your video? Do you do it in powerpoint, and do you use graphpad to make these graphs and figures? How do you also lay your graphs/figures on top of each other?

[pel]

Very nice and easy to understand, thanks

[nourjiheneagouf]

Thank you very much for this explanation !!!

[v.sakthivel]

Hi, process capability aim to achieve by consuming 50% tolerance

When the dats are LSL to USL range we can get P value


But we are fixing control limits how can get P value

[RicardoLopes]

Thank you very for your fantastic explanation!

[azmiansari]

If the data lets say score of student is not normally distributed then what will we do? Will we use non parametric test like Mann-Whitney?

[PunmasterSTP]

Testing for normality? More like "Terrific video that you gotta see!" 👍

Now I'm definitely curious about the specifics of the normality tests, but I bet they're rather complicated...