Suited’s A.I. calculates the candidate’s probability of success by comparing their assessment results and academic history to current employees.
The A.I. evaluates the hundreds of data points assessed for each candidate and employee. It then identifies over 10,000 potential statistical relationships between traits and job performance, including how traits interact with each other (such as if a candidate is both highly analytical and assertive), as well as non-linear relationships (such as if a moderate degree of curiosity is preferable to a high or low-degree of curiosity).
Sample Statistical Relationships Between Traits & Performance Considered
The candidate probability can be interpreted as the percentage of employees with similar traits and abilities who are high-performers at your firm.
<aside> 👉 For example, a candidate with a 70% Probability of Being a High-Performing Candidate indicates that 7/10 employees with similar traits and abilities are high-performers.
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The Suited Behavioral Score calculated for each applicant is statistically more predictive, and less biased, than other forms of candidate selection criteria. Because Suited measures significantly more factors than what is available on a resume and considers a wide variety of statistical patterns, Suited’s probabilities are 10-20x more accurate at identifying high-potential candidates than a resume screen alone on the basis of R-squared.
However, it is important to note that while Suited probabilities explain substantially more variance in employee performance than traditional factors like GPA and school ranking, there are many other factors that influence performance.
For example, Suited does not consider:
These factors and others will influence whether a candidate or employee is successful and should be evaluated during the recruiting process and after hiring where possible.