Two employees, same training, same method on paper. Yet one’s participants move on to work more often than the other’s. The quick conclusion is that one is simply better at the job. That conclusion might be right. It might also be completely wrong, and there’s no way to know which without looking closer.
Differences in results shouldn’t automatically be read as differences in performance. Competence is one possible explanation among several. Case complexity, workload, available support, and external factors can all affect the result, separately or together. Pointing to competence as the cause before the other factors are compared is guessing, not analysis.
Take a hypothetical example as an analytical framework, not as a typical explanation: an experienced employee has a smaller, more manageable group of participants, while a newer colleague carries a heavier workload that no one has accounted for. If leadership only sees the final result, the difference risks being read as a difference in skill, when it could just as easily stem from how the workload and support were distributed. The point isn’t that it’s always like this. The point is you don’t know until you’ve compared.
Four areas worth comparing before anyone draws conclusions about performance:
- Participant group and case complexity – Are the cases comparable in difficulty, or does one employee carry heavier or more complex cases?
- Workload – How many cases are handled at once, and how much time is there per case?
- Method support and supervision – How often does the employee get to check difficult situations with a colleague or manager?
- Documented method fidelity – Are the same steps actually carried out, in the same order and with the same consistency?
If the four areas are comparable and the results still differ clearly, competence or way of working becomes a more reasonable part of the explanation. If they’re not comparable, the difference in results mainly says something about the conditions, not about who’s most skilled.
It’s also a question of how you talk about results internally. If differences between employees are always read as differences in skill without first comparing the conditions, you build a culture where it becomes hard to raise that the workload is too high or that support is missing. Structural issues then risk never reaching leadership’s desk, because they’re hidden behind individual result numbers.
Making this kind of comparison a habit doesn’t require a big system. It requires collecting a few simple measures, for example the number of concurrent cases and how often support was actually given, alongside the results. Without that picture, every comparison between employees is incomplete, and every conclusion about who’s good or not stays uncertain.
Separating competence from conditions takes some courage from leadership. It’s easier to point to an individual employee’s results than to examine whether the team’s workload is uneven or whether the support structures are lacking. The organization that dares to ask that question gets, in return, a more accurate picture of where the real differences lie, instead of guessing based on who happens to stand out most in the statistics.
Want to test whether it holds true for you? Compare two employees with different results using the four areas above: participant group, workload, method support, and documented method fidelity. If they differ clearly, you’ve likely found part of the explanation, before drawing any conclusion about who’s best.