## Hypothesis Tests Under Separation This OSF project houses several documents that support my 2023 *Politial Analysis* paper titled "Hypothesis Tests Under Separation." **Pre-Print**: You can find the pre-print [here](https://osf.io/preprints/socarxiv/bmvnu/). **Reproduction Archive**: You can find a compressed reproduction archive [here](https://osf.io/9pbkd). Also on Dataverse at [https://doi.org/10.7910/DVN/6EYRJG](https://doi.org/10.7910/DVN/6EYRJG) **Computational Companion**: You can find the computational archive here [[HTML](https://osf.io/vnezd)] or here [[PDF](https://osf.io/sk5cq)] > Previous research suggests using penalized maximum likelihood for dealing with separation in logistic regression models (Zorn 2005), but notes that the penalty is a meaningful, substantive decision (Rainey 2016). In the project I show that researchers can use the likelihood ratio to compute reasonable, well-behaved *p*-values without using frequentist penalties or prior information.