By John Evelyn | August 17, 2009 |
Adversity,
Agility,
Blind Spots,
Capability,
Diagnosis,
Execution,
Fit for purpose,
Lean,
Rigidity,
Risks,
Six Sigma,
Transparency
We like symmetry. Most of us do. There is something in our wiring or programming that finds symmetry attractive, pleasing, and embodying some balance that might actually communicate harmony. We see it in the YinYang and the Taoist philosophy. We often characterize justice as a balanced scale, and countless studies have measured our perceptions of beauty among individuals and found facial symmetry the driving attractiveness variable. When we measure and analyze to find meaning in data, there is also an underlying “hope” that we find symmetry. When we see a “normal” distribution, or bell curve, we enter a comfort zone. In fact, I know countless people who work terribly hard at converting data that is not symmetric or normal into a set that is. Some, actually too many, take out data that does not fit the beauty of symmetry, proceed to insult it with names like outliers and dismiss them from our view.
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