Jun 12 2022
Perspectives On Probability In Operations
The spirited discussions on LinkedIn about whether probabilities are relative frequencies or quantifications of beliefs are guaranteed to baffle practitioners. They come up in threads about manufacturing quality, supply-chain management, and public health, and do not generate much light. Their participants trade barbs without much civility, and without actually exchanging on substance.
The latest one, by Alexander von Felbert, is among the more thoughtful, and therefore unlikely to inspire rants. I do, however, fault it with using words like “aleatory” or “epistemic” that I don’t think are helpful. I am trying to discuss it here in everyday language, and to apply the concepts to numerically specific cases, with an eye to operations.
While there are genuinely great and not-so-great ideas, the root of the most violent disagreements is elsewhere, with individuals generalizing from different experience bases. You may map probability to reality differently depending on whether you are developing drugs in the pharmaceutical industry, enhancing yield in a semiconductor process, or driving down dppms in auto parts. The math doesn’t care as long as you follow its rules, and it doesn’t invalidate other interpretations.
Oct 4 2022
Strange Statements About Probability Models | Don Wheeler | Quality Digest
In his latest column in Quality Digest, Don Wheeler wrote the following blanket statements, free of any caveat:
Source: Wheeler, D. (2022) Converting Capabilities, What difference does the probability model make? Quality Digest
Michel Baudin‘s comments:
Not all models assume i.i.d. variables
Wheeler’s first statement might have applied 100 years ago. Today, however, there are many models in probability that are not based on the assumption that data are “observations from a set of random variables that are independent and identically distributed”:
Probability Models Are Useful
In his second statement, Wheeler seems determined to deter engineers and managers from studying probability. If a prominent statistician tells them it serves no useful purpose, why bother? It is particularly odd when you consider that Wheeler’s beloved XmR/Process Behavior charts use control limits based on the model of observations as the sum of a constant and a Gaussian white noise.
Probability models have many useful purposes. They can keep from pursuing special causes for mere fluctuations and help you find root causes of actual problems. They also help you plan your supply chain and dimension your production lines.
Histograms are Old-Hat; Use KDE Instead
As Wheeler also says, “Many people have been taught that the first step in the statistical inquisition of their data is to fit some probability model to the histogram.” It’s time to learn something new, that takes advantage of IT developments since Karl Pearson invented the histogram in 1891.
Fitting models to a sample of 250 points based on a histogram is old-hat. A small dataset today is more 30,000 points, and you visualize its distribution with kernel density estimation(KDE), not histograms.
#donwheeler, #probability, #quality
By Michel Baudin • Press clippings • • Tags: Don Wheeler, Probability, Quality