The goal of this blog is to talk about how to best teach students how to successfully use applied statistics. During the last few weeks, and for the next few weeks, it is my plan to talk about a specific group of students: Administrators interested in making data driven decisions. During a prior blog, I reviewed the several components that should be part of such a training session. http://wp.me/pYrkk-d3
From experience, I have found that administrators are best able to learn about how to make data driven decisions only when they first learn about epistemology, how we know what we know, which was covered at this blog http://wp.me/pYrkk-d6, and cognitive biases, innate tendencies that keep us from accurately assessing what is going on around us. Business Insider summarized 56 organizational cognitive biases here, http://www.businessinsider.com/cognitive-biases-2014-6?op=1. Psychology Today reviews how cognitive biases negatively impact businesses, http://www.psychologytoday.com/blog/the-power-prime/201305/cognitive-biases-are-bad-business
Though there are many cognitive biases, in an organizational setting I like to speak about these 6.
- Confirmation Bias – we seek out information that matches what we already believe to be true, ignoring all information that contradicts our beliefs.
- Ingroup Bias – though quite complex, in short, we tend to prefer people we deem to be part of our group. We view them as more varied as people outside of our group. When they make mistakes we tend to be more forgiven or understanding. We tend to exert more energy to help them and protect them from harm.
- Projection Bias – what we think and feel is what others are thinking and feeling.
- Gambler’s Fallacy – that the risk we are about to take is going to pay off, especially after a series of bad events, as our luck is bound to change.
- Status-Quo Bias – most people are simply more comfortable when things stay the same, even if they are less than ideal. Organizational change is not comfortable for most people.
- Bandwagon Effect – I have heard people use the phrase, “sheeple” people who are following the herd regardless of what information might be saying otherwise.
What each of these cognitive biases have in common is that we are placed into a cognitive state where we ignore the data right in front of our nose, particularly if it is contradicting our firmly held belief. I was once in an administrative meeting after a particularly challenging decision, one for which the faculty were strongly against. And, yet, an administrator remarked that 80% of the faculty were on board with this decision. I didn’t know of one person, let alone 80% who were supportive of this decision outside of the people in the room, but a couple of cognitive biases were taking hold. The top administrators all felt this was a good idea, so with the bandwagon effect (among other pressures), so did the middle level administrators. Then, since they believed it to be a great decision for which they all agreed, they projected their thinking onto the vast majority of the faculty. A quick survey (formal or informal) would have helped them to see what the faculty were actually thinking. That information could have been used to either change their decision, weaken the intensity of the decision, or provide communication/justification as to why such a widely disagreed upon decision had to be implemented.
Properly designed measures and appropriate sampling techniques can yield great data that can be used to help provide insight to administrators to aid them in moving an organization forward.
Certainly, if we stick with our cognitive biases, we’ll feel better about ourselves, but that won’t help an organization become the best it can be, as in the end, an administrator is only as good as the decisions he or she is making.
In training administrators on how to best use applied statistics, start with explaining how data can help them achieving higher quality decisions by by-passing epistemological and cognitive bias limitations.