[EDIT MODE TO VIEW] Effect of Omitted Variable on Y Correlation Between Omitted Variable and X Direction of Bias Positive Positive Positive (Overestimation) Negative Positive Negative (Underestimation) Positive Negative Negative (Overestimation) Negative Negative Positive (Underestimation)

"confounding variables"

ReconfigBehSci. (2020, October 31). RT @learnfromerror: PHIL STAT FORUM NOVEMBER 19; STEPHEN SENN ‘Randomisation and Control in the Age of Coronavirus?’ https://t.co/hqT8GWTqC… [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1323311706609721344

Nerd, G. M.-K. H. (2021, December 29). Ivermectin Didn’t Save Uttar Pradesh From Covid-19. Elemental. https://gidmk.medium.com/ivermectin-didnt-save-uttar-pradesh-from-covid-19-17684f49d8b3

Hernán, M. A., Clayton, D., & Keiding, N. (2011). The Simpson’s paradox unraveled. International Journal of Epidemiology, 40(3), 780–785. https://doi.org/10.1093/ije/dyr041

November 19: “Randomisation and control in the age of coronavirus?” (Stephen Senn). (2020, October 30). PhilStatWars. https://phil-stat-wars.com/2020/10/30/november-19-randomisation-and-control-in-the-age-of-coronavirus-stephen-senn/

Confounding vs. Effect modification – The Stats Geek. (n.d.). Retrieved February 27, 2021, from https://thestatsgeek.com/2021/01/13/confounding-vs-effect-modification/

The one thing I'd note is the personality confound -- the people who get inspired by profs, seek out extra work, involve in extra-curriculars, etc. may just be people who approach life with a better attitude period, or may be from a social class that allowed them to do these things.