Although the dropout behavior and labor market opportunities of STEM (Science, Technology, Engineering, and Mathematics) students and the masculinization of STEM fields are all well-researched topics in STEM research, there is a very limited body of literature focusing on the social background and academic preparedness of STEM applicants. Thus, in this research, we compared STEM and non-STEM students based on their type of settlement, type of secondary school program, the rate of students coming from a disadvantaged background, extra points given for academic accomplishments, and total application score. To identify variables that significantly predict getting admitted to a STEM field, we conducted binary logistic regression. During our research, we conducted the analysis using the 2017 Hungarian Admission Database. Our sample consisted of those who got admitted to a full-time BA/BSc or undivided course (N = 41324). According to our results, STEM students cannot be identified as a disadvantaged group either in terms of their social background or their lack of academic preparedness. According to the results of the binary logistic regression, the main predictors of getting admitted to a STEM field are gender (male), having a language certificate, and having a vocational training certificate. The main goal of our research was to explore whether the individual characteristics of STEM students can be the reason behind the high attrition rates specific to STEM fields. Since our results did not support this conclusion, we suppose to further investigate the role of institutional variables (such as climate, the selective approach of college teachers, and high academic expectations) in dropouts.