A new, comprehensive preregistered meta-analysis found that, whether the diversity was demographic, cognitive, or occupational, its relationship with performance was near-zero.
These authors were very thorough
Just take a look at the meta-analytic estimates. These are in terms of correlations, and they are corrected for attenuation
These effect sizes are significant due to the large number of studies, but they are very low, even after blowing them up
You may ask yourself: are there hidden moderators?
The answer looks to be 'probably not.' Team longevity, industry sector, performance measures, power distance, year or country of study, task complexity, team interdependence, etc.
None of it really mattered.
Here's longevity:
Here's power distance:
Here's collectivism:
But let's put this into practical terms.
Using these disattenuated effects, if you selected from two groups you expected to have comparable performance otherwise, but one was more diverse, you'd make the 'correct' (higher-performing) decision in 51% of cases (vs. 50%).
That assumes there really hasn't been any bias in what gets published. If there has been, you might want to adjust your estimate downwards towards zero, or upwards if you think the literature was rigged the other way.
The paper paints an unsupportive picture of the idea that diversity on its own makes teams more performant.
Phenotyping is the vast, minimally-explored frontier in genome-wide association studies.
Important thread🧵
Briefly, phenotyping is how you measure people's traits. Measure poorly, get bad results; measure well, get good results.
Example? Janky knees.
The janky knee example refers to osteoarthritis, the most common form of arthritis, which occurs when the cartilage between bones is worn down, so bones start rubbing against each other.
This ends up being very painful.
Everyone with this condition isn't necessarily diagnosed with it.
This is especially true for men, who tend to just ignore this (and many other conditions) more often than women do.
This is, in a word, annoying, because it means that if you study it, sampling is likely biased.