I write about Apple. I have opinions about how Apple is doing as a company. How do I know that my judgments bear any resemblance to reality?
Recently, I was reminded of the required thinking process in a book that I’ve been reading in the evenings. The Big Picture. It’s by Dr. Sean Carroll, a theoretical physicist at the California Institute of Technology. In Chapter 9, author Carroll is preparing to talk about how scientists know what they know, and how reliable their knowledge is about certain things.
Stay with me here. There won’t be any math.
Author Carroll starts with an introduction to the Rev. Thomas Bayes (1702-1761) who was an English Presbyterian minister. Oh, and quite a good mathematician. We’re introduced to the methodology he worked out for “the best way of moving toward reliability in our understanding.”
On the same page, author Carroll more carefully defines the scientist’s argument that while they may not know everything, they know a lot of things. Here’s the quote that caught my eye as he answers the question about how we know the reliability of our understanding:
To even ask such a question is to admit that our knowledge, at least in part, is not perfectly reliable. This admission is the first step on the road to wisdom. The second step on that road is to understand that, while nothing is perfectly reliable, our beliefs aren’t all equally unreliable either. Some are more solid than others. A nice way of keeping track of our various degrees of belief, and updating them when new information comes our way, was the contribution for which Bayes is remembered today.
Degrees of Belief
Without repeating his Chapter 9, Carroll goes on to explain degrees of belief. Statisticians call theses credences. For example, if I told you that a man on a bicycle just rode past my house, knowing my location (Colorado) and your own world experience, you’d place a high credence on my casual remark.
However, if I told you that a headless man just rode by my house on a horse, you’d place a low credence on that fact. Later, I explain that a movie studio was filming a movie, the Legend of Sleepy Hollow, in my neighborhood. Suddenly, with this new information, your credence, your estimation of the validity of my remark, goes way up.
Thomas Bayes formalized this process in a way that lends itself to statistical assessment of validity. So, when physicists at the Large Hadron Collider at CERN reported in 2012 that they discovered the Higgs Boson, it was a statistical result, based on very high standard of confidence. This is a process that, without prior training or experience, is lost on most casual readers and authors.
Page 2: How does all this apply to Apple?