I've been revisiting several probability, statistics & clinical research concepts that I've learned through simulation & visualization over the past years. Full list pinned below - I hope this can be helpful for others who also learn this way! #epitwitter #statstwitter #RStats The "birthday problem"

https://twitter.com/BenYAndrew/status/1059178107310096384?s=20&t=_JN-GENavm8Oc17e7_3IJw

The assumption of normality in many statistical models refers to the model residuals and not the dependent variable. @oscar_olvera100 has a great post about this topic (linked). I tried to expand on his thoughts through some additional simulation/visualization. #epitwitter 1/n

https://twitter.com/oscar_olvera100/status/1124010789160009728

In these examples, we’ll look at a simple linear model Y ~ X, where X is dichotomous and Y is continuous. This first plot shows, for two relative effect sizes, the distributions of:

1. Y stratified by X
2. Y without stratification
3. Residuals from the linear model Y ~ X

2/n

I took a crack at reworking @statsepi’s RCT adjustment post (bit.ly/2Pd1JkH) w/ added simulation & visualization. To pique interest, here is a redacted version of the final image. Stick around for a walkthrough of how to get there & what it means. #epitwitter #rstats 1/n Let's say we are studying the effect of a treatment on a continuous outcome (Y). In this field of study, 20 continuous patient variables are commonly reported (i.e., are found in the typical table 1). 2/n