Curious Cat Management Improvement Blog: Deming, lean thinking, innovation, customer focus, continual improvement, six sigma.
February 12, 2007
Illusions - Optical and Other

Checkerbox Graphic If the output for working for the year is a square. And the job is to produce dark squares who do you pay more A or B? Of course it is a trick question, the squares are the same color. But it doesn’t look that way at first does it? Optical illusions provide evidence that you cannot always trust what seems obvious.

Dr. Deming’s red bead experiment provides some additional insight into the idea that our management systems often use “evidence” to support our believes when in fact the “evidence” does not mean what we think it does. Dr. Deming included the theory of knowledge (how do we know what we know) as one of the four areas of his management system. It is the areas of his work that is least appreciated and understood by managers today. Optical illusions provide a simple reminder of how easily we can think we know things that are not so.

Just as Toyota is always dissatisfied and looking for how to improve, it is important to question what you believe. Even when it is as obvious as the A square being darker than the B square. Understanding the ease with which we can reach false conclusions can be a powerful aid in improving management decision making.

Related: The Illusion of Understanding - Change is not Improvement - Performance Appraisal Problems - Dr. Deming on Performance Appraisal: “The fact is that the system that people work in and the interaction with people may account for 90 or 95 percent of performance” (from the introduction to the Team Handbook) - It is a mistake to think improving the figures is the goal

Optical illusion by Edward H. Adelson

2 Responses to “Illusions - Optical and Other”

  1. Curious Cat Science and Engineering Blog Says:

    Seeing Patterns Where None Exists
    “I call data dredge studies the ‘Rorschach tests’ of epidemiology, because researchers can pull out characteristics about people in almost unlimited combinations to find all sorts of correlations and conclude just a…”

  2. Curious Cat Science and Engineering Blog » Mistakes in Experimental Design and Interpretation Says:

    This is an excellent article discussing very common errors in how people use data. We have tendencies that lead us to draw faulty conclusions from data. Given that it is important to understand what common mistakes are made to help us counter the natural tendencies…

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