Historically, admission into pharmacy school was more selective and most students could feel confident in passing the NAPLEX no matter where they enrolled. However, the landscape has shifted. We’ve seen a rapid expansion in the number of pharmacy programs set against a sharp decline in the applicant pool. From the 2022-2023 application cycle, PharmCAS reported that 86.8% of applicants were accepted to a pharmacy program, significantly up from the 30-50% range 15-20 years ago. At the same time, NAPLEX pass rates are also declining, down to 74.3% in 2024 from 85.8% in 2020.
As a data nerd, I decided to analyze specific school attributes to see how they actually correlate with NAPLEX first-time pass rates. My goal is to provide a data-driven look at which factors truly influence your chances of passing the NAPLEX on the first try. I also want to show that data analysis and statistics doesn’t have to boring, and maybe even a little fun!
The Data
I made a data set from information available to me from PharmCAS, NABP, and pharmacy school websites. I compiled the type of program (public or private), program length (traditional 4-year vs. others), the age of the program (in years, as of 2024), class size (based on number of first attempts), and whether the program is part of a tier 1 or tier 2 research university to see if any of that could provide me with some insight on a program’s percentage of students who pass the NAPLEX on the first try.
One disclaimer: The pass rate data is from 2024, which means many students were enrolled during the COVID-19 pandemic. At some point I might find the time to update my data with 2025, especially since the mean pass rate came up that year, but let’s see what we can find with this anyway.
Visualizing Variables
I thought it might be fun to just explore some scatter plots and descriptive statistics. Right off the bat, it looks like there might be some correlation between a lower US News ranking (higher number) and lower pass rates.

Next, I looked at the program age. There also appears to be some correlation between the age of the program (with older programs being a higher number) and first time pass rate.

Some other interesting insights with the binary data: public institutions have a higher mean pass score relative to private institutions (80% vs. 72%, p<0.001) and 4-year programs have a higher mean pass score than other types of program lengths (77% vs. 71%, p=0.02).


Additionally, there appears to be some correlation between class size and pass rate (larger classes have higher pass rates) and between the age of the program and rank (older programs tend to be ranked higher).


Programs that are affiliated with tier 1 and tier 2 research institutions also tend to have higher mean pass rates than those that are not (81% for tier 1, 78% for tier 2, and 71% for others, p<0.001).

Mystery Solved?
On first glance, it looks like programs that are older, public, 4 years, and have a higher US News ranking are more likely to provide more of their graduates with a successful first-time pass rate. While these scatter plots show us a correlation, they don’t necessarily tell us the whole truth. For example: Does the 4-year structure actually help, or do those schools just happen to be the ones with higher research tiers or ranks? Does a school’s rank actually predict pass rates, or is the ranking just reflecting the fact that those schools are mostly large, public universities? When we account for the age of the program, do private schools actually perform differently than public ones, or are the lower pass rates we see just a symptom of being newer schools?
In Part 2, we will dive into some more robust analyses. I’ll share the results of a multiple linear regression to see which of these factors actually holds weight when we control for the others. We’ll also look at some diagnostics to make sure our model isn’t just hallucinating patterns (a little thing called collinearity). Stay tuned!


