We’re all guilty of these, at different times and in different ways. The cause is usually laziness rather than malice; either way, eternal vigilance is the best antidote.
Hubris
All models are wrong; but some are useful
Insensitivity
We’re consultants, not executors: respect the decision process of the client
Shaky Foundations
Something’s always wrong with the input data — whether outright data-entry errors or our misinterpretation of it
Opacity
Poor visualization practices hinder understanding; the most insightful analysis is easily obscured by the wrong form of display
Misdirection
Comparing the wrong variables; answering the wrong question
Pointlessness
Findings without a recommendation
Decoration
“Chart clutter” results from not understanding data visualization fundamentals
Inconsistency
Numbers don’t “tie” across pages
Uneconomic Measures
For example, optimizing accounting ratios instead of wealth measures
Misplaced Faith in Numbers
Numerical analysis drives the minority of decisions
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