The famous German theoretical physicist is the author of the statement That’s so wrong it’s not even wrong. It’s an important distinction, particularly since we live in a world where quantitative reasoning is increasingly relied on in making arguments or simply conveying information. It applies in other cases as well, where it is usually taken as an exasperated witticism, but it actually speaks to a basic problem not adequately addressed in classical logic: that is, a data based conclusion can be incorrect but it’s not false.
Here's a simple example. There's a a basic formula involving circles. So, to calculate the area of a circle you use the formula πr2, where r is the radius and π is a constant, whose approximate value is 3.14159265. But even that value of π is an approximation. Back in ancient times, π was usually shortened to 3.14. So the resulting answer is mathematically incorrect.
However, for the difference to have any real significance, the value of r has to be so large that, that for all practical purposes the difference between 3.14 and 3.14159265 is irrelevant. But the same argument holds for minor errors in measurement—in this case the actual value of the radius. I say that because if we’re making a real world calculation, in order to find r, we need to determine d (the diameter of the circle in question). And once we get out of school text exercises, have actual circles, those must be measured.
Precise measurement can be difficult. I discovered this distinction the hard way when I was studying chemistry. Back in late Medieval Period, when I was an undergraduate, the sequence of chemistry courses was year one: basic; year two: quantitative and volumetric analysis; year three: organic; year four: no bloody idea, I gave up.
But in the lab work for year two, the emphasis was on precise measurement, which was the necessary precondition for analysis.
Analysis: we’d be given a substance, have to use various tests to determine the amount of an unknown it contained. The answer was supposed to be correct to four decimal places, i.e., 2.3521 grams. Or maybe it was only 2.352 grams—after all these decades I can’t remember. Heh! If Dante had been one of my lab partners he would have added the lab for this course to one of his circles of Hell.
And of course in those days the calculations were done using a slide rule, so calculating an answer to anywhere close to the “correct” answer was pretty difficult (insert sarcastic laugh here).
I survived thanks to the advice given me by a chemistry major who was a flat-out genius. It was simple: spend all your time measuring, because if your answer was 2.687, it was a lot closer to the correct answer than 9.3562. It wasn’t correct, but it wasn’t false.
And when you look at what Professor Pauli actually said, I would argue that was exactly the situation in his mind, because the actual statement was Das ist nicht nur nicht richtig; es ist nicht einmal falsch! That is he’s opposing “not right” (nicht richtig) with “not even false” (nicht einmal falsch).
We live in a world where we’re constantly being given data driven conclusions, which means they’re all driven by numbers. And it’s interesting how many of those conclusions fall into the nicht einmal falsch category.
In an earlier post, I referred to the great English social historian G. M. Young’s remark in Victorian England that if Malthus had been right, everyone would have starved to death, and if Marx had been right, the monarchy would have been overthrown. QED: claims so wrong they weren’t even wrong. And as we all know, the followers of Malthus and Marx are still around.
Can the Not Even False Point Be Calculated?
But right off we have a major problem, because there’s no comprehensive data set with actual numbers for all arguments in this category, so all we have to go on is individual cases. So I’ll concentrate on a pretty important one: the economy of the Soviet Union.
Right before Stalin’s death, the official report on the grain harvest was 13o million tons. But when Khrushchev took over, he was skeptical about this figure, had it researched. And eventually, in 1958, it was decided that the actual figure was 92 million tons. So the real figure was obviously way off: the real figure was about 50 percent of the claim (or the claim overstated the real figure by 1.41 to 1. I would argue that an error of this magnitude is exactly what Professor Pauli was talking about, because the original reported figure was so far outside the actual figure.
During the Cold War, the Central Intelligence Agency devoted an enormous amount of energy to evaluating the Gross National Product of the Soviet Union and comparing it to the GNP of the United States, and then calculating the percentage of the GNP devoted to defense spending. As the USSR collapsed, these figures, and the process that produced them, were re-evaluated
This took place before two Russian economists, Nikolai Shmelev and Vladimir Popov, attempted an analysis of Soviet production figures. Their conclusion was that Soviet figures were off by ordinals, that is, instead of being “wrong” by 3 percent, it was more like 30 percent—or even higher, which the grain harvest example certainly supported. Nicht einmal falsch indeed.
For the mathematically inclined, that’s helpful, at least in this sense: a number that’s wrong by, say, 5 percent, while “not right,” is probably not in Pauli’s second category, while one that’s off 50 percent most probably is.
Of course in science there’s supposedly a built-in check: reproducibility. That is, the way we decide if the conclusion is correct is by repeating the experiment with the same data set. So far so good, but there’s an obvious problem: in a great many cases we can’t do that, as Nikita Sergeyevitch realized with the Soviet grain harvest.
The Failure of the Basic Method in Axtual Practice
But even in the world of supposedly controlled scientific experiments, it’s been pretty well established that for the last quarter of a century or so, a great many claims made have failed that test. This is known as the “Reproducibility Crisis.”
In fact, if you poke around, you find that the “Reproducibility Crisis” in science is a serious problem, and an embarrassing one.
In recent years, one major publisher of science journals has been forced to retract 11,300 published articles and close 19 journals. Although concerns about this problem rarely make their way into mass media, the problem not only exists, but it has been noted in major publications like Nature, The Economist, The Fiscal Times, The Wall Street Journal, The Atlantic, and The Telegraph, for well over a a quarter of a century.
For example the pioneering study of homosexuality done by Alfred Kinsey, with its claim that ten percent of the population has homosexual tendencies, although widely accepted, has been contradicted by dozens of studies, the consensus being that Kinsey’s figure was off by ordinals, just as was the case with the analysis of Soviet data that the Russian economists noted.
Meanwhile, Back in the USSR
Now Professor Pauli rightly didn’t supply any guidelines for deciding the actual difference between the two, but the CIA estimates definitely have the same difficulties I noted above. This process was an extremely complex and technical process, and if you’re interested in it, you can easily find the agency’s description of the process and the post postmortems relating to its accuracy.
So here’s a brief summary of the main conclusion from the years 1969 through 1973. In a (redacted and released) in house document, three key pieces of information were presented as a simply table. The first line was the estimate of the Soviet GNP, the second, the percentage spent on defense, and the third, the US GNP. The first and last lines were in dollars. So if you wanted to compare the two economies all you had to do was simple math (the top line is what percentage of the bottom line?).
When I looked at this I was curious as to why that hadn’t been done, so I calculated it myself. In 1969 the Soviet GNP was 50.54 percent of ours, but then for the next four years it was between 53 and 55 percent. But regardless, the percentage devoted to defense stayed the same: six percent.
Now in the postmortem, Charles Wolf, an economist at the RAND Corporation graded the CIA efforts, gave them a “strong C” for accuracy. But his estimate was that the actual Soviet percentage of the US GNP was was more like 30 percent overall, and the percentage of defense spending was likewise off—and by about the same amount; that is, their estimate of the Soviet GNP was about 1.7 times higher than it really was, and the estimate of defense spending was off by a around 2.25. So the degree of “wrong” exceeded what the Khrushchev regime had found about the grain harvest, and was in line with what Shmelev and Popov had concluded.
This was a C? If only he had been my chemistry lab instructor! But all joking aside, here are the two points.
Obviously, the estimates everyone was relying on were incorrect, and by a rather large amount. But given the complex nature of the problem, finally, the judgment as to whether the estimates were Pauli’s first case or his second is subjective. And given that complexity, Dr, Wolf’s “grade” possibly reflected that.
My own view, however, is that these estimates fit Professor Pauli’s “so wrong it isn’t even wrong”category, not least because they contributed to a view of the USSR (and Russia) that led to a totally false view of Moscow’s military power—the same view that led to the well nigh universal belief that Moscow's February 2022 attack on Ukraine would be a relatively short war that Moscow would win.
And to be fair to Professor Wolf, in his critique he explained why the CIA had produced those figures. He put it very tactfully, but at bottom his point was that the analysts had depended on a mistaken assumption about command driven economies, i.e., that they had the same efficiency as market drive ones.
But Pauli’s observation is still important, because there’s a natural tendency in us to assume that if we discount some amount of the claim, we’re reasonably close to the the correct answer. As the old saw (among economists) goes: an optimist looks at a glass that’s half filled with water and says it’s halfway full, a pessimist says it’s half empty, and an economist says “compared to what.” But if we follow Professor Pauli, the correct response would be “Assuming there’s even a glass.”
The Solzhenitsyn Solution
Aleksandr Isayevich spoke of this problem as the “fifty percent fallacy.” What he meant was that Soviet claims were so wrong they weren’t even wrong. Specifically, that even if you discounted them by half, your conclusion was still incorrect. In purely quantitative (numeric) terms, if you go back and analyze the numbers I’ve given you, that seems extreme.
But most probably all Aleksandr Isayevich was doing was factoring in important considerations that had been ignored—basically what the important psychologist Abraham Maslow had done when he told Kinsey his findings were fatally flawed.
In the Soviet case, four important factors. First, poor quality control. How many of those supposed 92 million tons of grain were actually usable? Grain can rot, and it can spoil in a variety of ways.
Second, corruption. How much of the grain actually reached the external and internal users it was supposed to benefit?
Third, ineptitude in distribution. Grain has to be processed to be useful to humans. That involves a whole chain of transportation and processing in order for it to be usable to consumers..
Fourth, the inferiority of the product. Some strains of grain are inherently more nutritious than others, as the result of climate, soil conditions, and the specific strains used.
So Aleksandr Isayevich was simply factoring all those in, and as someone who’d lived (and suffered) inside the system, he knew what he was talking about.
To illustrate the point in ways that are easier to grasp for most of us: in previous posts about the Russian military in Ukraine, I’ve pointed out that the marked inferiority of most Russian equipment when compared to its Western equivalent, the poor quality control, the lousy logistics, the obsolete tactics, and the failure to adapt to the dramatic changes in warfare.
Now I’m not bashing the CIA, or even the bureaucrats inside the Soviet State. Trying to reduce an economy to numbers is extremely complicated, involves all sorts of assumptions—even when the data isn’t corrupted. Working in a state where you were liable to disappear if you gave an answer that wasn’t what the authorities wanted to hear—and accuracy was beside the point—isn’t conducive to honest reporting.
And once you get outside of the tightly controlled world of controlled experiments, it’s extremely difficult to come up with answers that are anywhere near correct.
But finally, Professor Pauli was right: a lot of conclusions are so wrong they’re not even false. Unfortunately, researchers being human, they don’t like to admit their mistakes. As the American novelist Sail Bellow observed, “A great deal of intelligence can be invested in ignorance when the need for illusion is deep.”
I enjoy your expertise and your writings on current geopolitical happenings….but I’m scratching my head with this one.
What is the “real world relevance” with any of this information? Wolfgang who? Who really cares about any of his garble-di-gook?
Mmmm, sorry, I do not.