Type K Error

What statisticians call Type 1 errors (incorrectly rejecting the null hypothesis) and Type 2 errors (incorrectly accepting the null hypothesis) initially arose from signal detection theory: is that blip on the radar screen a signal or just noise? The two errors were known to us engineers (my former life) as either a false alarm or a missed detection.

But these are not the only statistical errors that can occur. Andrew Gelman proposed two additional statistical errors, Type S (confidently stating that a value is positive when it is negative, or vice versa) and Type M (confidently stating that a value is small in magnitude when it is large, or vice versa). They have less to do with the actual statistics than with interpretation of those statistics.

In furtherance of Gelman’s extension of statistical errors, I’d like to propose a new one, the Type K error. This is in recognition of the attempt by Kris Kobach (Kansas Secretary of State and vice chair of a federal voter fraud commission) to deny the vote to (at least)  tens of thousands of US citizens in order to prevent the two or three improper votes (out of millions cast) from occurring. [My numbers may be off, but you get my meaning.]

There have been other manifestations of this “error” in recent days. A report detailing the economic consequences of admitting refugees did not include the overwhelming financial benefits they provide over the long haul. In other words, the Type K error might be defined as “the deliberate and wrongful act associated with a statistical evaluation of the effect of only one side of a policy.”