William Poirier
banner
olsisblue.bsky.social
William Poirier
@olsisblue.bsky.social
PhD. candidate (ABD) @westernupolisci.bsky.social
MA from NYU and @universitelaval.bsky.social

Check my work here https://williampo1.github.io/
Nous nous sommes ainsi assuré de rendre le tout digeste par l'utilisation d'exemples, en limitant le jargon, et en répétant les concepts importants. 4/4
August 19, 2025 at 1:52 PM
ainsi que les analyses de sensibilités en contexte descriptif. Le lecteur est ainsi amené à parfaire sa compréhension de la logique statistique derrière l'un des outils de recherche les plus courants en science politique qui est trop souvent omis de la formation méthodologique. ... 3/4
August 19, 2025 at 1:52 PM
Nous abordons le concept d'ignorabilité des processus générateurs de données des sondages probabilistes et non probabilistes. Ceci nous permet d'illustrer les conditions permettant à la calibration de fonctionner; le lien entre la taille de l'échantillon, le PGD, et le biais de sélection; ... 2/4
August 19, 2025 at 1:52 PM
There are times though where plots are superior to tables such as in multiple comparisons. Not to toot my own horn, but that is exactly what we tried to accomplish here:
www.cambridge.org/core/journal...
Decoupling Visualization and Testing when Presenting Confidence Intervals | Political Analysis | Cambridge Core
Decoupling Visualization and Testing when Presenting Confidence Intervals
www.cambridge.org
April 24, 2025 at 2:27 PM
So, trying to add extra information contained in the table into the plot risk obscuring the message the plot was created for.
April 24, 2025 at 2:23 PM
My reservation here is that to me, the goal of visualization exercises is to render immediately apparent to a lay person what is hidden away in regression tables. A visualization will never fully satisfy quantitatively inclined scholars the way a table would. It's not their point.
April 24, 2025 at 2:22 PM
I'm with @dandekadt.bsky.social and @andrew.heiss.phd in that these looked like distributions of something at a first glance, but it's actually an ad hoc shading. Hence pairing them to posterior distributions would get the point across while being meaningful.
April 24, 2025 at 2:18 PM
Forgot to mention, Prof. Armstrong also prepared open access seminars on the topic where he dives a bit more into the weeds of the issue and its implementation and usage in R and Stata. Links below!
instats.org/seminar/usin...
instats.org/seminar/usin...
Using Confidence Intervals for Visual Testing in R (Free Seminar) - On-Demand
This Instats seminar on Using Confidence Intervals for Visual Testing in R (Free Seminar) taught by Dave Armstrong will help you take your research to the next level. Sign up today!
instats.org
January 17, 2025 at 6:38 PM
This is all implemented our VizTest package. You can install the R version from here: github.com/davidaarmstr...

And the Stata version here: github.com/davidaarmstr...
GitHub - davidaarmstrong/VizTest
Contribute to davidaarmstrong/VizTest development by creating an account on GitHub.
github.com
January 17, 2025 at 2:41 PM
Our procedure figures out what the visualized confidence level should be such that all pairwise tests are visually correct WHILE maintaining accurate comparison with 0 (the null hypothesis).
January 17, 2025 at 2:39 PM
You might want to visually test whether the estimates for July 2020 and July 2022 are statistically different from each other by looking at the overlap of their CI. You might know that it's wrong, but it's so tempting!
January 17, 2025 at 2:39 PM