The McNamara fallacy (or the quantitative fallacy) occurs when an arguer dismisses or ignores a potential contributing cause just because it is difficult to measure or quantify.

This fallacy is named after former US Secretary of Defense, Robert McNamara, because of the following story:

To create a report for President John F. Kennedy on the status of the Vietnam war, McNamara assembled some quantitative metrics, such as weekly body counts, leading to a conclusion that the US was "winning" the war. This conclusion turned out to be famously wrong.

At the time, McNamara had asked General Edward Landsdale if the list of metrics left out any variable that might be important. The general told him, Yes: the level of dedication and resolve of rural Vietnamese people toward resisting a US military presence.

After initially adding this to his list, McNamara crossed it off before doing his final analysis, telling the general it was "unmeasurable." McNamara later regretted this as a crucial mistake. In hindsight, McNamara could have collected subjective estimations of field commanders and averaged them week to week to get a general direction of this "unmeasurable" variable. This might have saved him from giving the President an inaccurate portrayal of the war.

Today, researchers sometimes call difficult-to-measure factors "squishy variables" because they are "subjective". But it may be better to collect multiple witness evaluations, say, on a 1-to-5 scale (despite how subjective this may seem), rather than give up on including the variable.

In critical thinking, whenever an arguer dismisses an opposing arguer's presentation of a causal factor just because it is hard to measure, we call it the McNamara fallacy.

Here is an example:

Medical researchers Tris and Gabe are viewing the results of a new drug they are testing.

Gabe: The data shows that this drug doesn't work any better than our old one. The survival rate is about the same after 6 months, 12 months, etc.

Tris: Yes, but in reading the comments from patients, it seems their quality of life is a little better with the new drug. So maybe the new drug is better, after all. Patients feel better and are happier.

Gabe: That's worthless. How are we going to measure such a thing? Let's just move on.

Gabe is committing the McNamara fallacy. It cannot be dismissed that the new drug might have an advantage over the old one, in respect of producing a better quality of life. Even though it may not be possible to measure this result with very much precision, that does not mean it isn't real, and relevant.

One general template of this fallacy would be:

1. Factor X may have an effect upon matter A, but is difficult or impossible to measure or quantify.

2. Therefore X should be disregarded or ignored when determining how to manage or predict matter A.

Image Credit: Wannapik under CC BY 3.0