Testing normality is pointless. Do this instead

[Saynotoclipontiescch]

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

[killamaniac]

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.

[yulia]

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)

[billyboy]

I found a new hidden gem channel! Nice video.

[perfectmoments]

I went on to telling my students to run hist(rnorm(n, 0, 1)) a few times with n being their sample size, to get a feeling of what would all be totally fine samples of normal distributions. If their residuals (lm) or samples (t.test) look like they would fit in there, they're good. What do you think of this approach?

[RichmondDarko-qome]

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?

[idodlek]

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?

[igorbione]

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

[Eloss]

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 ?

[deyvismejia]

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

[pipertripp]

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

[Tascioni]

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 😆

[Nyonyokki]

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?

[TheHeadincharge]

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.

[mohammedyounes]

I read in a book that p-values such as 0.25, 0.5, 0.75, and 0.95 lack a rigorous scientific foundation and are largely arbitrary and we’re using it just because some guy said so over 50 years ago.

[jishanzaman]

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

[TheJucuska]

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 ;)

[galenseilis]

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.

[naftalibendavid]

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?

[galenseilis]

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