When measured statistically.
This is not a takedown of that statement, but the reason we've trouble with this idea is that it works in the lab and not always in real life.
To set up a clean experiment, you have define what success looks like before you conduct the experiment - that the output variable is defined.
Once you know what to measure ahead of time to determine success, then statistical models tend to not be as random as a group of humans in achieving that target.
The variance is bad in an experiment, but variance jitter is needed in an ever changing world even if most variants are worse off.
For example, if you can predict someone's earning potential from their birth zipcode, it is not wrong and often more right than otherwise.
And then if you base student loans and business loan interest rates on the basis of birth zipcodes, the original prediction does become more right.
The experimental version that's a win, but in real life that's a terrible loss to society.