Jack Fitzgerald
jackfitzgerald.bsky.social
Jack Fitzgerald
@jackfitzgerald.bsky.social
Economics PhD candidate at Vrije Universiteit Amsterdam and the Tinbergen Institute. Working on applied econometrics, replication, and economics of science. https://jack-fitzgerald.github.io. Likes/reposts aren’t endorsements, views are my own.
Had a great time presenting my job market paper at the Lindau Nobel Meeting in Economic Sciences! 🔗 : osf.io/d7sqr_v1/

#LINOecon #EconSky
September 2, 2025 at 7:05 PM
What academic journal should I start? Wrong answers only
March 13, 2025 at 4:45 PM
I'll be waking up early (7 AM CET) on Tuesday, March 12 to present my job market paper at 5 PM Sydney time! If you're awake too, stop by to hear me talk about equivalence testing, replication-based methods research, and the robustness of null results in economics!
March 7, 2025 at 10:53 AM
We also offer the tst() command in the eqtesting R package, the tsti command in Stata, and Jamovi code. You can visit the paper to find download instructions for all, + guidelines for implementation. We hope you find it useful! (8/9)

osf.io/preprints/ps...
December 20, 2024 at 3:58 PM
To make things easy, we offer the ShinyTST app, a point-and-click Shiny app that tells you which test/confidence interval is relevant, provides p-values, and visualizes test results given an estimate, standard error, and SESOI. 7/9

jack-fitzgerald.shinyapps.io/shinyTST/
December 20, 2024 at 3:57 PM
Practical significance conclusions about an estimate can be easily inferred from double-banded confidence intervals that combine the estimate’s (1 - α) CI (e.g., its 95% CI) with its (1 - 2α) CI (e.g., its 90% CI). 6/9
December 20, 2024 at 3:54 PM
The three-sided testing (TST) framework combines two-sided minimum effects tests for inferiority/superiority with the two one-sided tests (TOST) equivalence testing procedure. TST can provide stat. sig. evidence that estimates are practically significant, or practically = 0. 4/9
December 20, 2024 at 3:53 PM
Estimates can also be stat. sig. bounded outside of Δ (e.g., blue estimate). What should we conclude about estimates like these blue/orange estimates? Standard equivalence testing frameworks don't give us clear answers. We introduce researchers to a framework that does. 3/9
December 20, 2024 at 3:52 PM
Equivalence testing lets us test whether estimates are stat. sig. bounded beneath practically negligible effect size Δ (e.g., pink estimate). But estimates can be both stat. sig. diff. from zero and stat. sig. bounded beneath Δ. 2/9
December 20, 2024 at 3:51 PM
New paper for holiday reading! @isager.bsky.social and I provide an introduction to three-sided testing, a framework for testing estimates' practical significance. We offer a tutorial, Shiny app, + commands/code in #Rstats, #Jamovi, + #Stata. 1/9

osf.io/preprints/psyarxiv/8y925
#EconSky #PsychSky
December 20, 2024 at 3:50 PM
In more recent news, I thoroughly enjoyed presenting The Need for Equivalence Testing in Economics at the Netherlands Reproducibility Network Symposium and Platform for Young Meta-Scientists Symposium, with great discussion from Tsz Keung Wong!
December 9, 2024 at 8:58 PM
Two weeks ago, I had a wonderful time presenting The Need for Equivalence Testing in Economics at the Leibniz Open Science Day! (pic: @prashantgarg.bsky.social)
December 9, 2024 at 8:55 PM
Does this count
December 2, 2024 at 9:48 PM
I had an excellent time presenting this paper to the Behavioural Insights for Business and Policy Network at the University of New South Wales. A huge thanks to @impartialspectator.bsky.social for hosting!
November 19, 2024 at 5:37 PM
I’ve also spent an extensive amount of time yelling about how stat. insig. bias estimates are really bad evidence that there’s negligible/zero bias. For an in-depth discussion, see my job market paper. 17/19
🔗: jack-fitzgerald.github.io/files/The_Ne...
November 18, 2024 at 4:08 PM
TE-irrelevant biases can badly misidentify TE-relevant biases, even up to the point of complete sign-flips. Trying to learn about hypothetical biases on TEs from hypothetical bias experiments that only vary stakes conditions can yield very misleading conclusions. 15/19
November 18, 2024 at 4:05 PM
Here’s a simulated example where hypothetical stakes increase the outcome’s standard deviation, but decrease the TE’s standard error. Just because your outcome is more precisely measured doesn’t necessarily mean your TE will be! 13/19
November 18, 2024 at 4:04 PM
This means that you can’t identify IHB in an experiment where you just randomize stakes conditions between groups and take differences in mean outcomes between those groups. If you try to infer IHBs from the CHBs estimated in these experiments, you can be badly misled. 11/19
November 18, 2024 at 4:02 PM
Intuitively, that’s for two reasons. 1) You can’t identify an interaction effect if all you know is the avg marginal effect of one of the variables in the interaction. 2) You shouldn’t expect hypothetical stakes to impact all interventions’ TEs on an outcome in the exact same way. 10/19
November 18, 2024 at 4:02 PM
I term the hypothetical bias relevant for TEs ‘interactive hypothetical bias (IHB)’, because it reflects the interaction effect between hypothetical stakes and the intervention of interest. CHB doesn’t identify this bias. 9/19
November 18, 2024 at 4:01 PM
In elicitation experiments, the only hypothetical bias we care about is the average marginal effect of hypothetical stakes on the outcome. I call this ‘classical hypothetical bias (CHB)’ because it’s the bias identified in most prior hypothetical bias studies. 7/19
November 18, 2024 at 4:00 PM
‘Intervention experiments’ do vary an intervention whose TE we care about. E.g., the same Becker-DeGroot-Marschak experiment w/ one product feature randomized between halves of the sample can give us estimates of that product feature’s impact on willingness to pay. 6/19
November 18, 2024 at 4:00 PM
‘Elicitation experiments’ vary no intervention: they just use experimental procedures to elicit (descriptive stats on) outcomes. E.g., Becker-DeGroot-Marschak experiments can elicit (average) willingness to pay for a product, but vary no intervention whose treatment effect (TE) we care about. 5/19
November 18, 2024 at 3:59 PM
There’s also recently been a wave of new studies showing that certain outcomes don’t stat. sig. differ between real-stakes and hypothetical-stakes experiments. These results are affecting thinking at the highest levels of experimental economics. 3/19
November 18, 2024 at 3:58 PM
Do real stakes/incentives matter in experiments? Recent studies say they don’t. My new paper shows that these studies’ results — and those of most hypothetical bias experiments — are uninformative when we care about treatment effects. 1/19
#EconSky #PsychSky #PoliSky
🔗: papers.tinbergen.nl/24070.pdf
November 18, 2024 at 3:56 PM