The Quantitative Fallacy Trap

Seasoned data scientists avoid the quantitative fallacy trap where you focus solely on certain quantitative metrics while ignoring other non-quantifiable variables. While the old saw that you cannot improve and manage what you cannot measure is true - what you decide to measure and not measure matters a great deal for understanding complex static, situational and fluid reality.

While today we have an abundance of data, we have a conceptual framework and data interpretation challenge. Raw data sets are not objective -  they are selected, collected, filtered, structured and analyzed by human design. What was measured, in what manner, with what devices and to what purpose? What was not measured and why? Was only low-hanging fruit measured because the important could not be measured? What was the quality of the data?

Naked and hidden biases in selecting and analyzing data present serious risks. How we interpret data and what elements to emphasize or ignore influences the quality of decision making. Ignoring non-quantifiable factors and attributes that may ultimately be more relevant to achieving the goal can lead to disaster.

Beware of falling into the quantitative fallacy trap.