Quantitative Data Problem Solving Essay

Quantitative Data Problem Solving Essay

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The use of quantitative data to solve a problem may seem as everyday and common sense-ish as any other problem solving style; perhaps even more so as it seems to make so much sense. First though – what exactly is quantitative data? It is measurable (through a suitable measure such as dollars, degrees, inches, millimeters) and verifiable data. It is however, amenable to statistical manipulation. Quantitative data defines whereas qualitative data describes (BusinessDictionary.com, 2010).
Why then, don’t we – as a society – use it more? It would seem that our biases toward everything from race and gender to reality and fantasy get in the way. One need look no further than the classic movie “12 Angry Men” (Rose & Lumet, 1957), about a dissenting juror in a murder trial who slowly manages to convince the others that the case is not as obviously clear as it seemed in court, to see that all is not as it seems to be. Even in the face of evidence (quantitative, circumstantial or peripheral), decisions and judgments – hence the solutions to the problem – are made with bias, prejudice and assumptive reasoning. Over the course of ninety-six minutes, we are enlightened to the role that bias plays in solving problems and making decisions, and the fact that the use of actual, quantifiable data and evidence can significantly influence – eventually – our ultimate decision.
If only we would give data a chance, we would be truly amazed at the outcome of its use. In the example of this movie, the actual knife, the stab wounds, the voice creating the screaming and the description of the alleged perpetrator running away all point away from the accused, but no matter. The bias of the jurors takes over. The biases trigger statements ...


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Broyles, W. (writer) and Howard, R. (director) (1995). Apollo 13. Imagine Entertainment.
Universal Studios. Hollywood: California.
Covey, S. (1989). The seven habits of highly effective people. Covey Leadership Center: Utah.
Guterman, J. (2002). Don’t throw good money (or time) after bad. Harvard Business Review.
Harvard Business School School Publishing Corporation: Cambridge.
Horace. (23 B.C.). The Odes: Greece.
Myers, I. and Briggs, K. (1962) Myers-Briggs Type Indicator. Consulting Psychologists Press:
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