Earlier quoted context omitted.
Your scientific take is useful in the case where selection bias is unavoidable and needs to be corrected for. This case is not like that; if the insurance agency wants to dispute the 90% false denial rate, it would be trivial for them to take a random sample of _all_ cases, go through the appeal process for those, and publish the resulting number without selection bias. As long as that doesn't happen, the most logica…
What do you think the claim approval rate is? Less than 10%? It stands to reason that the overwhelming majority of cases where the claim was approved were approved correctly. Unless that rate is well under 15%, it’s impossible to have the claimed “90% error rate”.
Did you comment because you take issue with misuse of the term error rate, or because you think that correct approvals make up for incorrect denials, and that therefore overall error rate is a useful metric?