Life By Numbers
Quantifiable measures like IQ and BMI don’t always tell the whole story
Posted May 20, 2019
Our lives are ruled by numbers. Scores, points, and metrics often end up determining our decisions or even defining our identities. Just like the IQ (Intelligence Quotient), which tells us how clever we are. Or the BMI (Body Mass Index), which is a measure of physical health. Or the number of Instagram followers, which often serves as a proxy for popularity. Then there is the credit score, which is used as a measure of net worth. The number of kids as an indicator of fertility. Running distance as a measure of stamina. And last but not least there is our monthly salary, which is typically treated as a core benchmark of professional success.
And isn’t this reliance on numbers oddly comforting? It provides much-desired clarity in a complex world of greyscales and uncertainties. IQ scores give an indicator about suitable jobs, BMI results tell us how much weight to lose (or gain) to maximise life expectancy, and Instagram followers put a number to our social status. All these metrics enable us to compare ourselves with others, set future goals and track process.
But is this always useful?
The McNamara Fallacy
The attractive simplicity of quantifiable measures can lead us to neglect other relevant factors, which are harder to assess. This results in a “quantitative bias”, which has been documented throughout human history. During the Vietnam War, for example, one politician, in particular, stood out due to his extreme obsession with numbers. Robert McNamara, then US Secretary of Defence, took a hard-data approach to measure success in the war, and judged progress solely by considering the total body count. He discounted any other, unquantifiable aspects such as public opinion, which led him to develop a dangerously narrow understanding of a tremendously complex conflict. His judgment bias was deemed so severe that his example was used to rename the quantitative fallacy: Today it is most commonly referred to as the McNamara fallacy.
Four Assumptions to Avoid
Are you worried about being lured into the quantitative data trap? There are four main assumptions, which can indicate a dangerous overreliance on quantifiable observations.
1. “Everything can be measured”
Different things are measured in different ways. A person’s height can be assessed easily by using a tape measure. A person’s feelings, on the other hand, are much harder to gauge. A common human misconception is that everything can and should be measured. Let’s consider this assumption in the context of professional success. Success is a vague concept and individuals vary greatly in their personal interpretations of the term. While some people are likely to list a high salary, others might highlight job security, possibilities for personal development or feelings of self-actualisation. With concepts as complex as this one, it seems unlikely that a single measure could capture it completely, and it may be futile to search for it.
2. “Any metric is better than none”
When writing a recent funding application, I was asked to provide evidence for my public engagement (i.e., any social interactions outside of my usual academic research circle). I obediently detailed my number of Twitter followers and blog readers only to second-guess this strategy a moment later. Was it really useful to list quantitative data in this context? Weren’t these figures misleading at best? The number of followers on social media (be it Twitter or Instagram) rarely tells you about the quality of any interactions. Some of these followers may be artificial bots, some may be inactive users. The same applies to numbers of blog readers, website clicks or interactions with advertisements. The mere count doesn’t tell you how much time people spent engaging with the contents, nor how meaningful the engagement was. Recognising these limitations of crude metrics is necessary for evaluating their use.
3. “Quantifiable targets are always useful”
Numeric targets can be useful for motivating behaviour change and the development of better habits. However, this is only the case if targets are chosen carefully to reflect the desirable end-goal. Consider the case of BMI targets to achieve a healthy body weight. BMI is a simple measure, calculated by dividing an individual’s weight by their height. It doesn’t distinguish between the weight of muscle mass and fat. With muscle tissue being denser and therefore heavier than fat, many professional athletes would come out “overweight” on the common BMI scale. Does this mean we should tell athletes to reduce their calorie intake and aim for a lower BMI score? Certainly not. Quantitative targets need to be used selectively with leeway for adaptation in special circumstances.
4. “Unquantifiable attributes are not important”
It is easy to forget about unquantifiable factors and dismiss them as unimportant. In the case of intelligence, the easy-to-calculate IQ score is often used exclusively to inform our judgment. However, this selective focus on quantifiable aspects invariably leads to an incomplete understanding of intelligence: While IQ scores give an indication of a person’s analytical abilities, they are unlikely to tell us much about their critical thinking skills or creativity, both of which may be central factors defining intelligence. A single-minded, quantitative focus could, therefore, result in serious misunderstandings of the underlying concepts, and should consequently be avoided.
Do you lead a life by numbers? Do you obsess about meeting arbitrary targets (like attending 10 yoga classes per week); worry excessively about test scores; or overthink general health metrics? Perhaps it’s time you tackled the underlying assumptions and created a more balanced approach to life.