Fit versus qualification

Substantial research has shown that we substitute a difficult decision with an easier one without realizing it.  When applying for colleges, graduate schools, or a job position, we cognitively understand the importance of finding the best fit – the culture, the environment, the location, the available resources.  This is an extraordinarily difficult question partly because we do not understand what constitutes “good fit.”  It is, however, much easier to look at metrics like USNews rankings, funding, or teacher-to-student ratios.  Advisors frequently recommend that applicants “go with the gut feeling,” not the numbers.

The same is true for those on the other side of the interview table.  It is difficult to prove whether a candidate is an excellent fit to the organization; it is much easier to evaluate their test scores, grades, recommendation letters.  Selection committee members are frequently cautioned against judging solely at the scintillating “objective data” on an application.

Medical school 101 teaches aspiring physicians to treat the patient, not their x-ray or labs.  Each individual piece of objective data contributes but does not replace good – albeit imperfect and subjective – clinical judgement.

The reason why this lesson is frequently repeated in almost every cognitive discipline is simple: it is very easy to forget that we are constantly making judgments using imperfect information.  Deciding whether you are qualified for a position is a metrics game – formal education, prestigious pedigree, ample experience.  But maybe we’ve had it wrong all along – maybe qualification is a threshold and not gradient.

Determining “fit” is a more process for which qualification is but one element – will you be happy being part of this organization?  Do you see yourself performing maximally in this setting? Because we place more emphysis on those we can measure, clinicians sometimes focus on small aberrations in laboratory values while forgetting the patient, and radiologists sometimes mull over the small findings regardless of their clinical significance, just as investors are frequently faulted for focusing on the day-to-day fluctuations of the market rather than on the overarching economic trend.

For the rest of us, we sometimes put unwarranted amount of emphasis on metrics-driven qualification and forget the fit because numbers are easier to interpret than people.

Howard Chen on GithubHoward Chen on LinkedinHoward Chen on Wordpress
Howard Chen
Vice Chair for Artificial Intelligence at Cleveland Clinic Diagnostics Institute
Howard is passionate about making diagnostic tests more accurate, expedient, and affordable through disciplined implementation of advanced technology. He previously served as Chief Informatics Officer for Imaging, where he led teams deploying and unifying radiology applications and AI in a multi-state, multi-hospital environment. Blog opinions are his own and in no way reflect those of the employer.

Leave a Reply