In twenty five years as a psychologist, I have never tested assumption of normality. Now I know I was right not to.

I found a new hidden gem channel! Nice video.

Hey man, this was a really interesting video. In my master's forever ago they never explicitly mentioned this idea but rather implied it the language used to evaluate models. Namely, using tests at the introductory stages to later saying how robust a model is to deviations of the underlying assumptions. Also I'm a huge fan of you're emphasis on diagnostics. The first few times in industry I encountered some bespoke model my company had been paying for I was greeted with all shoulders from management and customer services for the model providers when I asked for model diagnostics to be included. Drove me nuts.

as a russian person I think you nailed the russian accent! Well done :D and thanks for your videos! As a medical doctor and a big fan of statistics I really love your way of teaching people complicated stuff)

And if Kolmogorov-Smirnov says your residuals are not normally distributed, it's big trouble for moose and squirrel!

Oh my, this video would save me a lot of work if I checked earlier! Thanks!

Super useful, especially in ecology, because I rarely get normal data from my field experiments. And when I do, is usually because something went wrong 😆

Out of the topic but the video makes me think of it : why do we use Pearson correlation when modeling data ? Why not Kendall measure or even better, use Copulas ? Using Pearson looks to me like you know nothing about your variables interactions but you want to measure their linear interaction … you will obtain something but is it a useful information ?

Thank you very much for such informative videos. I spent several years in class and didn't understand all these concepts, but watching this video has made things easier for my comprehension. I have a few questions I would like to ask: When performing a statistical test, we use a parametric test if the data or variable in question is normally distributed, and a non-parametric alternative if the data or variable is not normally distributed. My question is: when does the central limit theorem come into play here? Also, a colleague of mine told me to always use parametric tests even if the data is not normally distributed. His explanation was that parametric tests are more powerful than non-parametric tests. So, should I straightforwardly use the non-parametric alternative when I observe that my data is not normally distributed, or should I take the CLT into consideration and use the parametric test?

Hello Mr. Fife 😀 Does, for example, running general linear model as t-test versus mann-whitney u test and comparing theirs results count as sensitivity analysis? Or only transformations, bootstraping and trimming would count as sensitivity analysis?

I'd love to follow your steps in R but flexplot is not compatible with my R version 4.3.2. Which version do you use?

Why do I feel personally attacked lol I like to test assumptions but great video!!

I’ve always wondered why we don’t look at effect size when running these tests at least to make them slightly more useful. Although, I would argue that is true for all parametric tests. Turkey’s quote about parametric tests has always been my favorite to help me understand this interpretation properly. Great video though, normality testing is truly the most misunderstood concept by most psychologists in my experience.

I don't see a link in the description to the data set. 🐕

@Quant Psych Where's the paper? :c

I've already imagined that one day you'll make a video on this topic...now I got that..thank u so much❤

Thanks for the video, it was great! You can also do one about the independence, because I had problems with it in my last rejected manuscript ;)

If you want your model to be as correct as possible, then you should aim for your model to do a good job of predicting the data distribution. Predicting the conditional expectation is a pretty rough approximation, especially with data sets like this where it is apparent that most of what is going on is not compressed well by a line.

I learned something today. Thank you. But too much comedyto the point that it is distracting.

The more power you have, the more power you have to show that your data aren't normal. GREAT! (But maybe a non-parametric...) What is a "meaningful" departure from normality? I don't know...is it big enough to make my real Type I error rate larger than my nominal alpha? Is it so far from normality that my power takes a beating?

You're actually telling me to overlook the p-values and use my brain to... think? Come on!

As Russian I may say your "Russian" pronunciation is adapted from the Snatch or similar quality spy series.

Dude, with all my respect to the depth of the content, could you please do accents more frequently?

So is all this proving that our model is robust to violations of the normality assumption? That class was back in 1995 and my professor said we assume normality, independence, and one other thing, but that if we violated one of those assumptions it wasn’t a big deal because our test was robust to those violations.

Second (but, more accurately third ;) )

I have to say that your Russian accent is pretty good

I want a version of your videos without the stupid comments. Instead of a 5 mins vid it became 20

First

Please, please don’t do silly voices, or other clown stuff. Its terribly annoying, and one reason I unsubscibed.

This dude is just yapping, don't waste your time