![]() The sample size is even larger this year than last year, but is still small in comparison to the actual number of test takers. The same limitations plague this data set as with the 2016 data set. Notably, Zanki is gaining market share from Bro's Anki deck. This is closely followed by NBME Practice Exams and Sketchy Micro (and to a lesser extent, Sketchy Pharm). As everyone suspected from anecdotal reports, NBME 19 is a terrible under-predictor of performance.ĭata on resource usage suggests UFAP (Uworld, First Ad, Pathoma) is go-to. UWSA 1 & 2 tend to overestimate the actual Step 1 score ( m 1, after pegging the intercept at 0). Eyeballing the trend-lines, it appears that the best single practice test for score prediction is the UWorld Self Assessment 2. Starting board review earlier is associated with a 2-4 point increase with each year you begin earlier.ĭesired specialty is also a great predictor of score, but this data set was collected after scores were released, and may thus affect specialty choice.Īll practice test scores showed positive correlation with the actual Step 1 score. IMGs scored similarly to US MD's (247 vs 245). Degree/Program suggests a minor 5 point jump between US DO's and US MD's (240 vs 245). Persons will see around a 10 point jump in their actual Step 1 score between the bottom 25th, middle 50's, and top 25th percentiles. According to the best-fit line, persons will generally do better than their goal score ( m = 1.0058, with the intercept pegged at 0). The biggest predictor of score (aside from practice exams) is your Goal Score and your Class Percentile. The national historical average for Step 1 is one standard deviation below the average of this data set. Standard Deviation: 14.25 Actual Step 1 Score vsĭistributions Resource Usage Percentile & Beginning Studying Distributions Interactive Data InterpretationĮveryone in this data set passed Step 1. Mean Actual Step 1 Score for this Sample: 244.02 Hope this helps everyone taking the 2018 USMLE Step 1. Thanks to everyone who participated, to u/Waygzh for the previous data set and analysis (which I obsessed over during dedicated), and to u/aervien for the data collection this year.
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