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The Good Doctor by Kenneth Brigham

The Good Doctor by Kenneth Brigham

Author:Kenneth Brigham
Language: eng
Format: epub
Tags: health;medicine;wellness;medical books;medicine books;psychology;medical;self help;neuroscience;coaching;spirituality;brain;personal development;mental health;parenting;communication;leadership;therapy;personal growth;ethics;how to;psych;business;social work;healing;philosophy;aging;relationships;education;social;sociology;mindfulness;technology;disability;healthcare;identity;anthropology;astrology;happiness;inspirational;nutrition;biology;economics;wisdom;spiritual;meditation;motivation;school
Publisher: Seven Stories Press
Published: 2020-05-13T18:16:27+00:00


CHAPTER 10

The Good, the Bad, and the Ugly of Statistics

When we use single numbers to estimate uncertain future outcomes . . . we are not just usually wrong, but are consistently wrong.

—from Harry Markowitz’s foreword to The Flaw of Averages: Why We

Underestimate Risk in the Face of Uncertainty by Sam L. Savage106

Here’s the good doctor’s problem with statistics. Statistical significance is the virtually universal criterion that medical doctors and scientists use to determine what is true. But the good doctor knows that statistics are perfectly capable of lying. Statisticians tacitly admit that. The American writer Darrel Huff even wrote a DIY manual on the subject that he titled How to Lie With Statistics,107 the best-selling statistics book of the second half of the twentieth century.

Statistics are about probabilities (how likely something is to be true or not) and the magic number is 0.05 (or 5 percent). If results have less than a five percent chance of being wrong (p<0.05; statistically significant in the jargon) then they are deemed likely to be true. But a probability of 0.05 means that there is a one in twenty chance that the results are totally random, not true at all. And how does your doctor know if you’re among the one in twenty for whom the truth is different than what the statistics say? Of course she doesn’t know until she learns everything she possibly can about you and maybe not until after some diagnostic and therapeutic trial and error. The problem is that statistics are meaningless for an N of 1; and you, I, and the other seven or so billion of our kind are each an N of 1.

One of us (MMEJ) practiced for many years as a cancer surgeon. His conversation with a patient newly diagnosed with a serious cancer might have gone something like this:

Patient: Well, doc, what are my chances of beating this thing?

MMEJ: Your chances of surviving this are either 0 or 100 percent.

Patient: What do you mean? Can’t you give me a number, some kind of odds?

MMEJ: If you want to know what percent of a large group of people with this cancer will survive it, I will of course give you a number. But for you personally, it’s all or none. You will either survive it or you won’t. So let’s be positive and assume that you’re a survivor.

Patient: I’m not sure whether I ought to feel optimistic or depressed.

MMEJ: Given the only alternative, don’t you think it makes the most sense to assume you’re going to beat it?

Patient: Maybe so, doc. Maybe so.

What this imaginary patient was told was true, but having convinced him that he and his doctor should set out a course of treatment assuming the best possible outcome, then what? None of the three major therapies for cancer—surgery, radiation, or drugs either separately or in some combination—is without serious collateral damage. So how do the doctor and the patient decide on a treatment that has the best chance of succeeding while doing



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