Apps

🧪 This platform is in early beta. Features may change and you might encounter bugs. We appreciate your patience!

McNamara Fallacy

Also Known As: quantitative fallacy metric fixation what gets measured gets managed (misattributed)
Statistical Error ID: mcnamara_fallacy

Definition

The McNamara Fallacy occurs when decision-making relies exclusively on quantitative metrics while ignoring qualitative factors that are harder to measure but equally or more important. Named after Robert McNamara, who used body counts as the primary measure of success in the Vietnam War, this fallacy proceeds in steps: first measuring what is easily measurable, then disregarding what cannot be measured, and finally assuming that what cannot be measured is not important.

Examples

A hospital evaluates doctors solely by the number of patients seen per hour. Dr. A sees 8 patients per hour with quick consultations. Dr. B sees 4 but spends time on thorough diagnosis and patient education. Management promotes Dr. A, ignoring that Dr. B has better patient outcomes and fewer readmissions.

A school district evaluates teacher performance exclusively through students' standardized test scores. Teachers begin 'teaching to the test,' cutting art, physical education, and class discussions. Test scores rise slightly, but student curiosity, mental health, and long-term love of learning quietly deteriorate — none of which appear in the metrics.

A social media platform measures content quality purely by engagement metrics: likes, shares, and watch time. Content optimized for outrage and shock performs best by these measures, and the algorithm promotes it heavily — while nuanced, accurate, and constructive content, which is harder to quantify, steadily disappears from feeds.

Verification Steps
Verification Steps
Binary yes/no questions that an AI must answer to detect a reasoning pattern in a text.
Each of the 452 aspects has verification steps — simple yes/no questions designed to systematically detect whether a pattern appears in a text. For ad hominem: "Does the argument attack a person rather than their claim?" For false dichotomy: "Are only two options presented when more exist?" This ensures consistent, reproducible analysis.

Binary (yes/no) questions an LLM must answer to identify this aspect:

  1. 1

    Is a decision being made based primarily on quantitative metrics?

    Type: binary
  2. 2

    Are important qualitative, subjective, or hard-to-measure factors being ignored?

    Type: binary
  3. 3

    Does the metric actually capture what matters for the decision?

    Type: binary
Deep Dive
The expandable detail section on each aspect page with examples, psychology, and counter-strategies.
The Deep Dive section provides in-depth information about each aspect: a real-world example showing the pattern in action, an explanation of why it works psychologically, practical advice on how to counter it, alternative names, and links to related aspects.

Hierarchical Context