Analytical and Scientific Arrogance

It is customary in democratic countries to deplore expenditures on armaments as conflicting with the requirements of the social services. There is a tendency to forget that the most important social service that a government can do for its people is to keep them alive and free.

Marshal of the Royal Air Force Sir John Slessor, Strategy for the West

I’m returning to the past to make a timeless point: Analysis is a tool of decision-making, not a substitute for it.

That’s a point to which every analyst will subscribe, just as every judicial candidate will claim to revere the Constitution. But analysts past and present have tended to read their policy preferences into their analytical work, just as too many judges real their political preferences into the Constitution.

What is an analyst? Someone whose occupation requires him to gather facts bearing on an issue, discern robust relationships among the facts, and draw conclusions from those relationships.

Many professionals — from economists to physicists to so-called climate scientists — are more or less analytical in the practice of their professions. That is, they are not just seeking knowledge, but seeking to influence policies which depend on that knowledge.

There is also in this country (and in the West, generally) a kind of person who is an analyst first and a disciplinary specialist second (if at all). Such a person brings his pattern-seeking skills to the problems facing decision-makers in government and industry. Depending on the kinds of issues he addresses or the kinds of techniques that he deploys, he may be called a policy analyst, operations research analyst, management consultant, or something of that kind.

It is one thing to say, as a scientist or analyst, that a certain option (a policy, a system, a tactic) is probably better than the alternatives, when judged against a specific criterion (most effective for a given cost, most effective against a certain kind of enemy force). It is quite another thing to say that the option is the one that the decision-maker should adopt. The scientist or analyst is looking a small slice of the world; the decision-maker has to take into account things that the scientist or analyst did not (and often could not) take into account (economic consequences, political feasibility, compatibility with other existing systems and policies).

It is (or should be) unsconsionable for a scientist or analyst to state or imply that he has the “right” answer. But the clever arguer avoids coming straight out with the “right” answer; instead, he slants his presentation in a way that makes the “right” answer seem right.

A classic case in point is they hysteria surrounding the increase in “global” temperature in the latter part of the 20th century, and the coincidence of that increase with the rise in CO2. I have had much to say about the hysteria and the pseudo-science upon which it is based. (See links at the end of this post.) Here, I will take as a case study an event to which I was somewhat close: the treatment of the Navy’s proposal, made in the early 1980s, for an expansion to what was conveniently characterized as the 600-ship Navy. (The expansion would have involved personnel, logistics systems, ancillary war-fighting systems, stockpiles of parts and ammunition, and aircraft of many kinds — all in addition to a 25-percent increase in the number of ships in active service.)

The usual suspects, of an ilk I profiled here, wasted no time in making the 600-ship Navy seem like a bad idea. Of the many studies and memos on the subject, two by the Congressional Budget Office stand out a exemplars of slanted analysis by innuendo: “Building a 600-Ship Navy: Costs, Timing, and Alternative Approaches” (March 1982), and “Future Budget Requirements for the 600-Ship Navy: Preliminary Analysis” (April 1985). What did the “whiz kids” at CBO have to say about the 600-ship Navy? Here are excerpts of the concluding sections:

The Administration’s five-year shipbuilding plan, containing 133 new construction ships and estimated to cost over $80 billion in fiscal year 1983 dollars, is more ambitious than previous programs submitted to the Congress in the past few years. It does not, however, contain enough ships to realize the Navy’s announced force level goals for an expanded Navy. In addition, this plan—as has been the case with so many previous plans—has most of its ships programmed in the later out-years. Over half of the 133 new construction ships are programmed for the last two years of the five-year plan. Achievement of the Navy’s expanded force level goals would require adhering to the out-year building plans and continued high levels of construction in the years beyond fiscal year 1987. [1982 report, pp. 71-72]

Even the budget increases estimated here would be difficult to achieve if history is a guide. Since the end of World War II, the Navy has never sustained real increases in its budget for more than five consecutive years. The sustained 15-year expansion required to achieve and sustain the Navy’s present plans would result in a historic change in budget trends. [1985 report, p. 26]

The bias against the 600-ship Navy drips from the pages. The “argument” goes like this: If it hasn’t been done, it can’t be done and, therefore, shouldn’t be attempted. Why not? Because the analysts at CBO were a breed of cat that emerged in the 1960s, when Robert Strange McNamara and his minions used simplistic analysis (“tablesmanship”) to play “gotcha” with the military services:

We [I was one of the minions] did it because we were encouraged to do it, though not in so many words. And we got away with it, not because we were better analysts — most of our work was simplistic stuff — but because we usually had the last word. (Only an impassioned personal intercession by a service chief might persuade McNamara to go against SA [the Systems Analysis office run by Alain Enthoven] — and the key word is “might.”) The irony of the whole process was that McNamara, in effect, substituted “civilian judgment” for oft-scorned “military judgment.” McNamara revealed his preference for “civilian judgment” by elevating Enthoven and SA a level in the hierarchy, 1965, even though (or perhaps because) the services and JCS had been open in their disdain of SA and its snotty young civilians.

In the case of the 600-ship Navy, civilian analysts did their best to derail it by sending the barely disguised message that it was “unaffordable”. I was reminded of this “insight” by a colleague of long-standing who recently proclaimed that “any half-decent cost model would show a 600-ship Navy was unsustainable into this century.” How could a cost model show such a thing when the sustainability (affordability) of defense is a matter of political will, not arithmetic?

Defense spending fluctuates as function of perceived necessity. Consider, for example, this graph (misleadingly labeled “Recent Defense Spending”) from usgovernmentspending.com, which shows defense spending as a percentage of GDP for fiscal year (FY) 1792 to FY 2017:

What was “unaffordable” before World War II suddenly became affordable. And so it has gone throughout the history of the republic. Affordability (or sustainability) is a political issue, not a line drawn in the sand by an smart-ass analyst who gives no thought to the consequences of spending too little on defense.

I will now zoom in on the era of interest.

CBO’s “Building a 600-Ship Navy: Costs, Timing, and Alternative Approaches“, which crystallized opposition to the 600-ship Navy estimates the long-run, annual obligational authority required to sustain a 600-ship Navy (of the Navy’s design) to be about 20-percent higher in constant dollars than the FY 1982 Navy budget. (See Options I and II in Figure 2, p. 50.) The long-run would have begun around FY 1994, following several years of higher spending associated with the buildup of forces. I don’t have a historical breakdown of the Department of Defense (DoD) budget by service, but I found values for all-DoD spending on military programs at Office of Management and Budget Historical Tables. Drawing on Tables 5.2 and 10.1, I constructed a constant-dollar of DoD’s obligational authority (FY 1982 = 1):

FY Index
1983 1.08
1984 1.13
1985 1.21
1986 1.17
1987 1.13
1988 1.11
1989 1.10
1990 1.07
1991 0.97
1992 0.97
1993 0.90
1994 0.82
1995 0.82
1996 0.80
1997 0.80
1998 0.79
1999 0.84
2000 0.86
2001 0.92
2002 0.98
2003 1.23
2004 1.29
2005 1.28
2006 1.36
2007 1.50
2008 1.65
2009 1.61
2010 1.66
2011 1.62
2012 1.51
2013 1.32
2014 1.32
2015 1.25
2016 1.29
2017 1.34

There was no inherent reason that defense spending couldn’t have remained on the trajectory of the middle 1980s. The slowdown of the late 1980s was a reflection of improved relations between the U.S. and USSR. Those improved relations had much to do with the Reagan defense buildup, of which the goal of attaining a 600-ship Navy was an integral part.

The Reagan buildup helped to convince Soviet leaders (Gorbachev in particular) that trying to keep pace with the U.S. was futile and (actually) unaffordable. The rest — the end of the Cold War and the dissolution of the USSR — is history. The buildup, in other words, sowed the seeds of its own demise. But that couldn’t have been predicted with certainty in the early-to-middle 1980s, when CBO and others were doing their best to undermine political support for more defense spending. Had CBO and the other nay-sayers succeeded in their aims, the Cold War and the USSR might still be with us.

The defense drawdown of the mid-1990s was a deliberate response to the end of the Cold War and lack of other serious threats, not a historical necessity. It was certainly not on the table in the early 1980s, when the 600-ship Navy was being pushed. Had the Cold War not thawed and ended, there is no reason that U.S. defense spending couldn’t have continued at the pace of the middle 1980s, or higher. As is evident in the index values for recent years, even after drastic force reductions in Iraq, defense spending is now about one-third higher than it was in FY 1982.

John Lehman, Secretary of the Navy from 1981 to 1987, was rightly incensed that analysts — some of them on his payroll as civilian employees and contractors — were, in effect, undermining a deliberate strategy of pressing against a key Soviet weakness — the unsustainability of its defense strategy. There was much lamentation at the time about Lehman’s “war” on the offending parties, one of which was the think-tank for which I then worked. I can now admit openly that I was sympathetic to Lehman and offended by the arrogance of analysts who believed that it was their job to suggest that spending more on defense was “unaffordable”.

When I was a young analyst I was handed a pile of required reading material. One of the items was was Methods of Operations Research, by Philip M. Morse and George E. Kimball. Morse, in the early months of America’s involvement in World War II, founded the civilian operations-research organization from which my think-tank evolved. Kimball was a leading member of that organization. Their book is notable not just a compendium of analytical methods that were applied, with much success, to the war effort. It is also introspective — and properly humble — about the power and role of analysis.

Two passages, in particular, have stuck with me for the more than 50 years since I first read the book. Here is one of them:

[S]uccessful application of operations research usually results in improvements by factors of 3 or 10 or more…. In our first study of any operation we are looking for these large factors of possible improvement…. They can be discovered if the [variables] are given only one significant figure,…any greater accuracy simply adds unessential detail.

One might term this type of thinking “hemibel thinking.” A bel is defined as a unit in a logarithmic scale corresponding to a factor of 10. Consequently a hemibel corresponds to a factor of the square root of 10, or approximately 3. [p. 38]

Morse and Kimball — two brilliant scientists and analysts, who had worked with actual data (pardon the redundancy) about combat operations — counseled against making too much of quantitative estimates given the uncertainties inherent in combat. But, as I have seen over the years, analysts eager to “prove” something nevertheless make a huge deal out of minuscule differences in quantitative estimates — estimates based not on actual combat operations but on theoretical values derived from models of systems and operations yet to see the light of day. (I also saw, and still see, too much “analysis” about soft subjects, such as domestic politics and international relations. The amount of snake oil emitted by “analysts” — sometimes called scholars, journalists, pundits, and commentators — would fill the Great Lakes. Their perceptions of reality have an uncanny way of supporting their unabashed decrees about policy.)

The second memorable passage from Methods of Operations Research goes directly to the point of this post:

Operations research done separately from an administrator in charge of operations becomes an empty exercise. [p. 10].

In the case of CBO and other opponents of the 600-ship Navy, substitute “cost estimate” for “operations research”, “responsible defense official” for “administrator in charge”, and “strategy” for “operations”. The principle is the same: The CBO and its ilk knew the price of the 600-ship Navy, but had no inkling of its value.

Too many scientists and analysts want to make policy. On the evidence of my close association with scientists and analysts over the years — including a stint as an unsparing reviewer of their products — I would say that they should learn to think clearly before they inflict their views on others. But too many of them — even those with Ph.D.s in STEM disciplines — are incapable of thinking clearly, and more than capable of slanting their work to support their biases. Exhibit A: Michael Mann, James Hansen (more), and their co-conspirators in the catastrophic-anthropogenic-global-warming scam.


Related posts:
The Limits of Science
How to View Defense Spending
Modeling Is Not Science
Anthropogenic Global Warming Is Dead, Just Not Buried Yet
The McNamara Legacy: A Personal Perspective
Analysis for Government Decision-Making: Hemi-Science, Hemi-Demi-Science, and Sophistry
The Limits of Science (II)
The Pretence of Knowledge
“The Science Is Settled”
Verbal Regression Analysis, the “End of History,” and Think-Tanks
Some Thoughts about Probability
Rationalism, Empiricism, and Scientific Knowledge
AGW in Austin?
The “Marketplace” of Ideas
My War on the Misuse of Probability
Ty Cobb and the State of Science
Understanding Probability: Pascal’s Wager and Catastrophic Global Warming
Revisiting the “Marketplace” of Ideas
The Technocratic Illusion
AGW in Austin? (II)
Is Science Self-Correcting?
“Feelings, Nothing More than Feelings”
Words Fail Us
“Science” vs. Science: The Case of Evolution, Race, and Intelligence
Modeling Revisited
The Fragility of Knowledge
Global-Warming Hype
Pattern-Seeking
Babe Ruth and the Hot-Hand Hypothesis
Hurricane Hysteria
Deduction, Induction, and Knowledge
Much Ado about the Unknown and Unknowable
A (Long) Footnote about Science
Further Thoughts about Probability
Climate Scare Tactics: The Mythical Ever-Rising 30-Year Average
A Grand Strategy for the United States

A True Scientist Speaks

I am reading, with great delight, Old Physics for New: A Worldview Alternative to Einstein’s Relativity Theory, by Thomas E. Phipps Jr. (1925-2016). Dr. Phipps was a physicist who happened to have been a member of a World War II operations research unit that evolved into the think-tank where I worked for 30 years.

Phipps challenged the basic tenets of Einstein’s special theory of relativity (STR) in Old Physics for New, an earlier book (Heretical Verities: Mathematical Themes in Physical Description), and many of his scholarly articles. I have drawn on Old Physics for New in two of my posts about STR (this and this), and will do so in future posts on the subject. But aside from STR, about which Phipps is refreshingly skeptical, I admire his honesty and clear-minded view of science.

Regarding Phipps’s honesty, I turn to his preface to the second edition of Old Physics for New:

[I]n the first edition I wrongly claimed awareness of two “crucial” experiments that would decide between Einstein’s special relativity theory and my proposed alternative. These two were (1) an accurate assessment of stellar aberration and (2) a measurement of light speed in orbit. Only the first of these is valid. The other was an error on my part, which I am obligated and privileged to correct here. [pp. xi-xii]

Phipps’s clear-minded view of science is evident throughout the book. In the preface, he scores a direct hit on the pseudo-scientific faddism:

The attitude of the traditional scientist toward lies and errors has always been that it is his job to tell the truth and to eradicate mistakes. Lately, scientists, with climate science in the van, have begun openly to espouse an opposite view, a different paradigm, which marches under the black banner of “post-normal science.”

According to this new perception, before the scientist goes into his laboratory it is his duty, for the sake of mankind, to study the worldwide political situation and to decide what errors need promulgating and what lies need telling. Then he goes into his laboratory, interrogates his computer, fiddles his theory, fabricates or massages his data, etc., and produces the results required to support those predetermined lies and errors. Finally he emerges into the light of publicity and writes reports acceptable to like-minded bureaucrats in such government agencies as the National Science Foundation, offers interviews to reporters working for like-minded bosses in the media, testifies before Congress, etc., all in such a way as to suppress traditional science and ultimately to make it impossible….

In this way post-normal science wages pre-emptive war on what Thomas Kuhn famously called “normal science,” because the latter fails to promote with adequate zeal those political and social goals that the post-normal scientist happens to recognize as deserving promotion…. Post-normal behavior seamlessly blends the implacable arrogance of the up-to-date terrorist with the technique of The Big Lie, pioneered by Hitler and Goebbels…. [pp. xii-xiii]

I regret deeply that I never met or corresponded with Dr. Phipps.

Not-So-Random Thoughts (XV)

Links to the other posts in this occasional series may be found at “Favorite Posts,” just below the list of topics.

*     *     *

Victor Davis Hanson writes:

This descent into the Dark Ages will not end well. It never has in the past. [“Building the New Dark-Age Mind,” Works and Days, June 8, 2015]

Hamson’s chronicle of political correctness and doublespeak echoes one theme of my post, “1963: The Year Zero.”

*     *     *

Timothy Taylor does the two-handed economist act:

It may be that the question of “does inequality slow down economic growth” is too broad and diffuse to be useful. Instead, those of us who care about both the rise in inequality and the slowdown in economic growth should be looking for policies to address both goals, without presuming that substantial overlap will always occur between them. [“Does Inequality Reduce Economic Growth: A Skeptical View,” The Conversible Economist, May 29, 2015]

The short answer to the question “Does inequality reduce growth?” is no. See my post “Income Inequality and Economic Growth.” Further, even if inequality does reduce growth, the idea of reducing inequality (through income redistribution, say) to foster growth is utilitarian and therefore morally egregious. (See “Utilitarianism vs. Liberty.”)

*     *     *

In “Diminishing Marginal Utility and the Redistributive Urge” I write:

[L]eftists who deign to offer an economic justification for redistribution usually fall back on the assumption of the diminishing marginal utility (DMU) of income and wealth. In doing so, they commit (at least) four errors.

The first error is the fallacy of misplaced concreteness which is found in the notion of utility. Have you ever been able to measure your own state of happiness? I mean measure it, not just say that you’re feeling happier today than you were when your pet dog died. It’s an impossible task, isn’t it? If you can’t measure your own happiness, how can you (or anyone) presume to measure and aggregate the happiness of millions or billions of individual human beings? It can’t be done.

Which brings me to the second error, which is an error of arrogance. Given the impossibility of measuring one person’s happiness, and the consequent impossibility of measuring and comparing the happiness of many persons, it is pure arrogance to insist that “society” would be better off if X amount of income or wealth were transferred from Group A to Group B….

The third error lies in the implicit assumption embedded in the idea of DMU. The assumption is that as one’s income or wealth rises one continues to consume the same goods and services, but more of them….

All of that notwithstanding, the committed believer in DMU will shrug and say that at some point DMU must set in. Which leads me to the fourth error, which is an error of introspection….  [If over the years] your real income has risen by a factor of two or three or more — and if you haven’t messed up your personal life (which is another matter) — you’re probably incalculably happier than when you were just able to pay your bills. And you’re especially happy if you put aside a good chunk of money for your retirement, the anticipation and enjoyment of which adds a degree of utility (such a prosaic word) that was probably beyond imagining when you were in your twenties, thirties, and forties.

Robert Murphy agrees:

[T]he problem comes in when people sometimes try to use the concept of DMU to justify government income redistribution. Specifically, the argument is that (say) the billionth dollar to Bill Gates has hardly any marginal utility, while the 10th dollar to a homeless man carries enormous marginal utility. So clearly–the argument goes–taking a dollar from Bill Gates and giving it to a homeless man raises “total social utility.”

There are several serious problems with this type of claim. Most obvious, even if we thought it made sense to attribute units of utility to individuals, there is no reason to suppose we could compare them across individuals. For example, even if we thought a rich man had units of utility–akin to the units of his body temperature–and that the units declined with more money, and likewise for a poor person, nonetheless we have no way of placing the two types of units on the same scale….

In any event, this is all a moot point regarding the original question of interpersonal utility comparisons. Even if we thought individuals had cardinal utilities, it wouldn’t follow that redistribution would raise total social utility.

Even if we retreat to the everyday usage of terms, it still doesn’t follow as a general rule that rich people get less happiness from a marginal dollar than a poor person. There are many people, especially in the financial sector, whose self-esteem is directly tied to their earnings. And as the photo indicates, Scrooge McDuck really seems to enjoy money. Taking gold coins from Scrooge and giving them to a poor monk would not necessarily increase happiness, even in the everyday psychological sense. [“Can We Compare People’s Utilities?,” Mises Canada, May 22, 2015]

See also David Henderson’s “Murphy on Interpersonal Utility Comparisons” (EconLog, May 22, 2015) and Henderson’s earlier posts on the subject, to which he links. Finally, see my comment on an earlier post by Henderson, in which he touches on the related issue of cost-benefit analysis.

*     *     *

Here’s a slice of what Robert Tracinski has to say about “reform conservatism”:

The key premise of this non-reforming “reform conservatism” is the idea that it’s impossible to really touch the welfare state. We might be able to alter its incentives and improve its clanking machinery, but only if we loudly assure everyone that we love it and want to keep it forever.

And there’s the problem. Not only is this defeatist at its core, abandoning the cause of small government at the outset, but it fails to address the most important problem facing the country.

“Reform conservatism” is an answer to the question: how can we promote the goal of freedom and small government—without posing any outright challenge to the welfare state? The answer: you can’t. All you can do is tinker around the edges of Leviathan. And ultimately, it won’t make much difference, because it will all be overwelmed in the coming disaster. [“Reform Conservatism Is an Answer to the Wrong Question,” The Federalist, May 22, 2015]

Further, as I observe in “How to Eradicate the Welfare State, and How Not to Do It,” the offerings of “reform conservatives”

may seem like reasonable compromises with the left’s radical positions. But they are reasonable compromises only if you believe that the left wouldn’t strive vigorously to undo them and continue the nation’s march toward full-blown state socialism. That’s the way leftists work. They take what they’re given and then come back for more, lying and worse all the way.

See also Arnold Kling’s “Reason Roundtable on Reform Conservatism” (askblog, May 22, 2015) and follow the links therein.

*     *     *

I’ll end this installment with a look at science and the anti-scientific belief in catastrophic anthropogenic global warming.

Here’s Philip Ball in “The Trouble With Scientists“:

It’s likely that some researchers are consciously cherry-picking data to get their work published. And some of the problems surely lie with journal publication policies. But the problems of false findings often begin with researchers unwittingly fooling themselves: they fall prey to cognitive biases, common modes of thinking that lure us toward wrong but convenient or attractive conclusions. “Seeing the reproducibility rates in psychology and other empirical science, we can safely say that something is not working out the way it should,” says Susann Fiedler, a behavioral economist at the Max Planck Institute for Research on Collective Goods in Bonn, Germany. “Cognitive biases might be one reason for that.”

Psychologist Brian Nosek of the University of Virginia says that the most common and problematic bias in science is “motivated reasoning”: We interpret observations to fit a particular idea. Psychologists have shown that “most of our reasoning is in fact rationalization,” he says. In other words, we have already made the decision about what to do or to think, and our “explanation” of our reasoning is really a justification for doing what we wanted to do—or to believe—anyway. Science is of course meant to be more objective and skeptical than everyday thought—but how much is it, really?

Whereas the falsification model of the scientific method championed by philosopher Karl Popper posits that the scientist looks for ways to test and falsify her theories—to ask “How am I wrong?”—Nosek says that scientists usually ask instead “How am I right?” (or equally, to ask “How are you wrong?”). When facts come up that suggest we might, in fact, not be right after all, we are inclined to dismiss them as irrelevant, if not indeed mistaken….

Given that science has uncovered a dizzying variety of cognitive biases, the relative neglect of their consequences within science itself is peculiar. “I was aware of biases in humans at large,” says [Chris] Hartgerink [of Tilburg University in the Netherlands], “but when I first ‘learned’ that they also apply to scientists, I was somewhat amazed, even though it is so obvious.”…

One of the reasons the science literature gets skewed is that journals are much more likely to publish positive than negative results: It’s easier to say something is true than to say it’s wrong. Journal referees might be inclined to reject negative results as too boring, and researchers currently get little credit or status, from funders or departments, from such findings. “If you do 20 experiments, one of them is likely to have a publishable result,” [Ivan] Oransky and [Adam] Marcus [who run the service Retraction Watch] write. “But only publishing that result doesn’t make your findings valid. In fact it’s quite the opposite.”9 [Nautilus, May 14, 2015]

Zoom to AGW. Robert Tracinski assesses the most recent bit of confirmation bias:

A lot of us having been pointing out one of the big problems with the global warming theory: a long plateau in global temperatures since about 1998. Most significantly, this leveling off was not predicted by the theory, and observed temperatures have been below the lowest end of the range predicted by all of the computerized climate models….

Why, change the data, of course!

Hence a blockbuster new report: a new analysis of temperature data since 1998 “adjusts” the numbers and magically finds that there was no plateau after all. The warming just continued….

How convenient.

It’s so convenient that they’re signaling for everyone else to get on board….

This is going to be the new party line. “Hiatus”? What hiatus? Who are you going to believe, our adjustments or your lying thermometers?…

The new adjustments are suspiciously convenient, of course. Anyone who is touting a theory that isn’t being borne out by the evidence and suddenly tells you he’s analyzed the data and by golly, what do you know, suddenly it does support his theory—well, he should be met with more than a little skepticism.

If we look, we find some big problems. The most important data adjustments by far are in ocean temperature measurements. But anyone who has been following this debate will notice something about the time period for which the adjustments were made. This is a time in which the measurement of ocean temperatures has vastly improved in coverage and accuracy as a whole new set of scientific buoys has come online. So why would this data need such drastic “correcting”?

As climatologist Judith Curry puts it:

The greatest changes in the new NOAA surface temperature analysis is to the ocean temperatures since 1998. This seems rather ironic, since this is the period where there is the greatest coverage of data with the highest quality of measurements–ARGO buoys and satellites don’t show a warming trend. Nevertheless, the NOAA team finds a substantial increase in the ocean surface temperature anomaly trend since 1998.

….

I realize the warmists are desperate, but they might not have thought through the overall effect of this new “adjustment” push. We’ve been told to take very, very seriously the objective data showing global warming is real and is happening—and then they announce that the data has been totally changed post hoc. This is meant to shore up the theory, but it actually calls the data into question….

All of this fits into a wider pattern: the global warming theory has been awful at making predictions about the data ahead of time. But it has been great at going backward, retroactively reinterpreting the data and retrofitting the theory to mesh with it. A line I saw from one commenter, I can’t remember where, has been rattling around in my head: “once again, the theory that predicts nothing explains everything.” [“Global Warming: The Theory That Predicts Nothing and Explains Everything,” The Federalist, June 8, 2015]

Howard Hyde also weighs in with “Climate Change: Where Is the Science?” (American Thinker, June 11, 2015).

Bill Nye, the so-called Science Guy, seems to epitomize the influence of ideology on “scientific knowledge.”  I defer to John Derbyshire:

Bill Nye the Science Guy gave a commencement speech at Rutgers on Sunday. Reading the speech left me thinking that if this is America’s designated Science Guy, I can be the nation’s designated swimsuit model….

What did the Science Guy have to say to the Rutgers graduates? Well, he warned them of the horrors of climate change, which he linked to global inequality.

We’re going to find a means to enable poor people to advance in their societies in countries around the world. Otherwise, the imbalance of wealth will lead to conflict and inefficiency in energy production, which will lead to more carbon pollution and a no-way-out overheated globe.

Uh, given that advanced countries use far more energy per capita than backward ones—the U.S.A. figure is thirty-four times Bangladesh’s—wouldn’t a better strategy be to keep poor countries poor? We could, for example, encourage all their smartest and most entrepreneurial people to emigrate to the First World … Oh, wait: we already do that.

The whole climate change business is now a zone of hysteria, generating far more noise—mostly of a shrieking kind—than its importance justifies. Opinions about climate change are, as Greg Cochran said, “a mark of tribal membership.” It is also the case, as Greg also said, that “the world is never going to do much about in any event, regardless of the facts.”…

When Ma Nature means business, stuff happens on a stupendously colossal scale.  And Bill Nye the Science Guy wants Rutgers graduates to worry about a 0.4ºC warming over thirty years? Feugh.

The Science Guy then passed on from the dubiously alarmist to the batshit barmy.

There really is no such thing as race. We are one species … We all come from Africa.

Where does one start with that? Perhaps by asserting that: “There is no such thing as states. We are one country.”

The climatological equivalent of saying there is no such thing as race would be saying that there is no such thing as weather. Of course there is such a thing as race. We can perceive race with at least three of our five senses, and read it off from the genome. We tick boxes for it on government forms: I ticked such a box for the ATF just this morning when buying a gun.

This is the Science Guy? The foundational text of modern biology bears the title On the Origin of Species by Means of Natural Selection, or the Preservation of Favored Races in the Struggle for Life. Is biology not a science?

Darwin said that populations of a species long separated from each other will diverge in their biological characteristics, forming races. If the separation goes on long enough, any surviving races will diverge all the way to separate species. Was Ol’ Chuck wrong about that, Mr. Science Guy?

“We are one species”? Rottweilers and toy poodles are races within one species, a species much newer than ours; yet they differ mightily, not only in appearance but also—gasp!—in behavior, intelligence, and personality. [“Nye Lied, I Sighed,” Taki’s Magazine, May 21, 2015]

This has gone on long enough. Instead of quoting myself, I merely refer you to several related posts:

Demystifying Science
AGW: The Death Knell
Evolution and Race
The Limits of Science (II)
The Pretence of Knowledge
“The Science Is Settled”
The Limits of Science, Illustrated by Scientists
Rationalism, Empiricism, and Scientific Knowledge
AGW in Austin?

Signature

“The Science Is Settled”

Thales (c. 620 – c. 530 BC): The Earth rests on water.

Aneximenes (c. 540 – c. 475 BC): Everything is made of air.

Heraclitus (c. 540 – c. 450 BC): All is fire.

Empodecles (c. 493 – c. 435 BC): There are four elements: earth, air, fire, and water.

Democritus (c. 460 – c. 370 BC): Atoms (basic elements of nature) come in an infinite variety of shapes and sizes.

Aristotle (384 – 322 BC): Heavy objects must fall faster than light ones. The universe is a series of crystalline spheres that carry the sun, moon, planets, and stars around Earth.

Ptolemey (90 – 168 AD): Ditto the Earth-centric universe,  with a mathematical description.

Copernicus (1473 – 1543): The planets revolve around the sun in perfectly circular orbits.

Brahe (1546 – 1601): The planets revolve around the sun, but the sun and moon revolve around Earth.

Kepler (1573 – 1630): The planets revolve around the sun in elliptical orbits, and their trajectory is governed by magnetism.

Newton (1642 – 1727): The course of the planets around the sun is determined by gravity, which is a force that acts at a distance. Light consists of corpuscles; ordinary matter is made of larger corpuscles. Space and time are absolute and uniform.

Rutherford (1871 – 1937), Bohr (1885 – 1962), and others: The atom has a center (nucleus), which consists of two elemental particles, the neutron and proton.

Einstein (1879 – 1955): The universe is neither expanding nor shrinking.

That’s just a small fraction of the mistaken and incomplete theories that have held sway in the field of physics. There are many more such mistakes and lacunae in the other natural sciences: biology, chemistry, and earth science — each of which, like physics, has many branches. And in all branches there are many unresolved questions. For example, the Standard Model of particle physics, despite its complexity, is known to be incomplete. And it is thought (by some) to be unduly complex; that is, there may be a simpler underlying structure waiting to be discovered.

Given all of this, it is grossly presumptive to claim that climate science is “settled” when the phenomena that it encompasses are so varied, complex, often poorly understood, and often given short shrift (e.g., the effects of solar radiation on the intensity of cosmic radiation reaching Earth, which affects low-level cloud formation, which affects atmospheric temperature and precipitation).

Anyone who says that climate science is “settled” is either ignorant, stupid, or a freighted with a political agenda.