Deltoid

A couple of weeks ago Xrlq wrote about me:

He’s the Australian blogger who aspires to do to John Lott what Clayton Cramer did to Michael Bellesiles. Unfortunately, he doesn’t do a very good job; while Cramer uncovered overwhelming evidence Bellesiles’s fundamental research was fabricated, the best Lambert has been able to do is to uncover a few really stupid things Lott has done on a few isolated occasions. The rest of his rebuttals consist of gratuitous attacks on Lott personally.

I felt that this was incorrect, so I remonstrated.with Xrlq in comments. After a lengthy discussion Xrlq ended up agreeing that it was wrong and had the decency to post:

Tim Lambert’s ‘Hat of the Day award has been revoked and conferred on me, instead, for having issued it in the first place. That will teach me to issue ‘Hats to bloggers.

Kevin Baker was one of the bloggers who posted on the story about Lindsay’s sword killing, claiming that it showed that for all intents and purposes self-defence in the UK was illegal. Despite learning that Lindsay had chased the robber out of his home and stabbed him in the back four times, in the comments and on his blog Baker continued to insist that self defence was illegal in practice in the UK. His argument was that England’s “laws concerning weapons make self-defense, for all intents and purposes, a lost cause”. His argument is badly wrong for two reasons.

1. Using a weapon is not the only way to defend yourself.
2. If the law disarms attackers, then it can make self defence possible where it would have been impossible if the attacker was armed.

Baker’s response on the first point is to focus on cases where a weapon might actually be the only way to defend yourself, for example, “a 90 pound woman who’s never been in a physical confrontation” versus “a 200-pound intruder who’s previously killed able-bodied men with his bare hands”. However, his claim was that self defence was generally impossible, not just in particular cases like a small women against a crazed killer.

On the second point he responds with:

Restrictions on weapons, except in rare cases, ONLY make it more difficult to defend oneself. They have essentially no effect on the access to weapons by violent criminals.

Baker offers no evidence for this claim. In his book Targetting Guns, Kleck calls this the fallacy of “The Overmotivated Criminal”. Not all or even most criminals are absolutely determined to get guns. Kleck writes:

Like noncriminals, however, criminals do many things that are casually or only weakly motivated. Indeed, much crime is impulsive or opportunistic, with criminals committing some crimes only if it requires little effort and entails little risk. Gun control is less likely to have much effect on crime committed by criminals with the strongest and most persistent motivation to commit crimes, such as drug dealers, emotionally disturbed mass murderers, professional hit men, terrorists, or political assassins. However, it is not all impossible for crime prevention efforts to be achieved among the more weakly or temporarily motivated criminals who make up the large part of the active offender population.

Baker than claims that the restrictions on weapons have made violent crime increase:

As a result of this physical reality, violent crime has been on the increase in England and Wales since the 1950’s.

I’m afraid that this claim is another example of the same phenomenon that led all the progunners to misinterpret the Lindsay stabbing. Because crime goes up and down and because they are many different categories of crime (such as murder, robbery, gun crimes, and so on) as well as both national and regional crime statistics, you can pretty well always find some crime statistic in the UK that has gone up. Those are the stories that American pro-gunners report. You never seem to find them reporting on the crime decreases. In fact, violent crime in England and Wales has decreased significantly since the 90s.

The Myers-Briggs Type Indicator (MBTI) attempts to classify each pesonality into one of 16 types described by the four letter codes in the table below. TypeLogic has descriptions of all the types, as well as a FAQ. You can find your out own type in this on-line test. If you have a blog you can enter your type in the table below.

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When I wrote earlier about Steve Milloy, I commented on his attack on a study that found that the introduction safe-storage laws was followed by a 23% reduction in unintentional shooting deaths of children. Milloy claimed:

The reported 23% decrease in injuries is a pretty weak result-probably beyond the capability of the ecologic type of study to reliably detect. Even in the better types of epidemiology studies (i.e., cohort and case-control), rate increases of less than 100% (and rate decreases of less than 50%) are very suspect.

Milloy repeats this factor-of-two principle many times on junkscience.com. For example, on this page Milloy asserts:

Relative risks from 1.0 - 2.0 should be ignored.

(This page explains what a “relative risk” is if you don’t know.)

In my earlier post I observed that Milloy somehow neglected to apply this factor-of-two principle to Lott’s work. Today I want to write about the origins of his principle. It’s a very interesting story.

When I first read his comments I was rather puzzled. A measure that reduced crime by 45% would be a pretty spectacular success, but by Milloy’s principle it would be ignored. If you look in statistics text books you will not find Milloy’s principle. You will find that two sorts of significance are important:

Statistical Significance

Is it likely that the result occured by chance? A result that has less than a 5% probability of occuring by chance is usually considered statistically significant. (Although values other than 5% could be used.)

Practical Significance

Does it make a difference that matters? A measure that only made a difference of a handful of crimes in the whole country probably isn’t worth worrying about.

Another important thing statistics texts will tell you is that correlation is not the same thing as causation. Just because the safe-storage law was followed by a 23% drop in injuries it doesn’t follow that the law caused the drop. Some other factor might have caused the drop. Some people misunderstand this to mean that correlation doesn’t have anything to do with causation. Correlation doesn’t prove causation, but it is evidence for causation.

Milloy’s factor of 2 principle arises from neither sort of significance. Larger factors are more likely to be statistically significant, but a factor of 2 can easily by statistically significant. If we are talking about a very rare crime, a factor of two change might not be practically significant, but for more common ones it most certainly would be. Finally, larger factors are stronger evidence for causation. There aren’t many things that make a factor of ten difference, so if we find a correlation with a factor of ten difference, it’s unlikely to really be caused by something else, while things that make a factor of two difference are more common, so factors of that size are more likely to be really caused by something else, but that certainly does not mean that they should be “ignored”.

The only authority that Milloy offers in support of his principle that risks of less than a factor of two should be ignored is an out-of-context quote from a National Cancer Institute press release about a study finding a link between breast cancer and abortion. If you look at the whole press release you will see that they are not saying that all risks of less than a factor of two should be ignored, but that a risk of less than two along with other evidence suggests that the link was spurious (as subsequent work found). Milloy even complains that the NCI didn’t follow his principle in other cases.

That brings me to an amazing story that was revealed in the Philip Morris documents archive. You see, in 1992 the EPA concluded that passive smoking caused lung cancer with a risk factor of about 1.25 for a non-smoker with a smoking spouse. Philip Morris obviously wanted to discount this finding. If only epidemiology guidelines included Milloy’s factor-of-two principle, then they could point to them and dismiss the EPA’s result. So Philip Morris set out to get the epidemiologists to adopt Milloy’s principle.

They funded the creation of TASSC and junkscience.com. Milloy used junkscience.com to energetically attack the EPA’s passive smoking conclusions and promote the factor-of-two principle. They also organised a series of seminars to try to get the scientific community to adopt what they called what they called “Good Epidemiology Practices” (GEP). The GEP guidelines were mostly perfectly reasonable things like

3. Statements of study design should contain a description of statistical techniques.

However, slipped into the middle of the GEP guidelines was this:

8. Odds ratios of 2 or less should be treated with caution, particularly when the confidence intervals are wide. There is a likelihood that the odds ratio is artefactual and the result of problems with case or control selection, confounders or bias.

The reaction of the scientists to the GEP guidelines was something like this:

“Excellent idea! We need guidelines for good practice and these fit the bill. We should adopt them…

Oh, except for number 8 about odds ratios. That doesn’t make sense so we’ll drop that one.”

Philip Morris kept pushing its GEP guidelines to various scientific organizations for several years, but eventually they realized that it just wasn’t going to work, as explained in this internal memo:

Approximately three years ago, the concept of GEP’s was discussed in considerable detail in PM. Corporate Affairs thought it was a wonderful idea, because at first they … felt that part of a code for Good Epidemiological Practices would state that any relative risk of less than 2 would be ignored. This is of course not the case. No epidemiological organization would agree to this, and even Corporate Affairs realizes this now.

The full story of GEP, with copious references to Philip Morris’ internal documents is detailed in a paper published in the American Journal of Public Health.

The fact that the Philip Morris executives thought that their GEP plan had a chance of succeeding tells us something about how they think science is conducted. The scientists did not adopt Milloy’s factor-of-two principle because it was, well, wrong. The Philip Morris executives thought that the truth of something did not matter to the scientists—you could get them to say something just by lobbying them. This attitude seems common to promoters of “sound science”. They seem to think that real scientists aren’t interested in finding out what is true or false but instead just concoct results to advance a poltical agenda or get more funding. In other words, they think real scientists operate like they do.

Efforts to promote Milloy’s bogus factor-of-two principle continue to this day. Just last month Iain Murray published an article where he wrote:

Epidemiologists generally agree that one cannot ascribe medical causation to a risk factor if the factor is associated with less than double the occurrence than normal.

No, epidemiologists do not “generally agree” with this. In fact, Philip Morris’ efforts to get then to agree with this proposition have proven that do not agree with it at all.

And where was Murray’s article published? Tech Central Station, another astroturf operation like junkscience.com. And who employs Murray? The Competitive Enterprise Institute, which is partly funded by Philip Morris.

The latest quiz to sweep blogspace is this quiz, which tests your ability to distinguish between quotes from comments at Little Green Footballs and quotes from Late German Fascists. Matt Yglesias’ post on the quiz triggered an extremely ill-tempered comment thread. In the spirit of my previous quiz pages, this one lets you post your score on the LGF quiz.

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In July last year, Lott, armed with no evidence at all, claimed that Washington DC had a higher murder rate than Baghdad. Faced with overwhelming evidence to the contrary, Lott stuck to his guns, even demanding that the New York Times “correct” an article and use Lott’s bogus murder rate. The whole discussion is here.

The New York Times has updated its figures:

	April	July	October	January
Annualized Murder Rate in Baghdad per 100,000 (DC rate 43)	70	130	100	100

The authors also explain how they worked out the murder rate:

Our best estimates on murder rates in Baghdad — a difficult calculation given that many Iraqi families are burying their own dead without notifying the authorities — indicate some improvement, but they are still far higher than in the most crime-ridden American cities. These murder numbers, it’s worth noting, are compiled using data from the Baghdad morgue, a wide array of news accounts and our conversations with American officials in Washington and Iraq. (Despite repeated requests, the Pentagon has not provided us with any figures of its own.)

Interesting. Lott claims he obtained his contradictory figures from the Pentagon.

A couple of days ago, I told the story of GEP, how a tobacco company tried to get epidemiologists to adopt a bogus principle that risk factors of less than 2should be ignored. I noted that Iain Murray was still peddling this bogus principle in a Tech Central Station article. That wasn’t the only time Murray had tried advancing the tobacco company’s risk-factor-of-two principle. He also did it in this Tech Central Station article, which prompted an actual epidemiologist to send him the following email:

Thank you for your thoughtful article “Epidemiology beyond its limits”, which highlighted some of criticisms of my discipline as it is currently practiced. I agree in general that epidemiologic research should be of the highest quality and conclusions should be reported in context. However, your article leaves the lay reader the impression that epidemiologists have been changing the rules for some reason. Not so.

First, the Bradford-Hill criteria were always meant to be rules of thumb, except temporality, which is metaphysically necessary. The BMA statement merely boils down to the proposition that an association may still be causal if one of the the criteria (save temporality) is not fulfilled. Put another way, fullfillment of most of the criteria can be sufficient to surmise causality. I think that is quite different from the impression you’ve given your readers, which is that 5 of 7 steadfast rules have been chucked by the side of the road.

Second, the NEJM quite explicitly told Gary Taubes that it was a rule of thumb that they only accepted papers with relative risks of 3 or more. I presume that since JAMA uses a similar rule of thumb, one of the things that played into their decision to publish the air pollution study with an RR of 1.12 is that it was very large, it was longitudinal, and it was methodologically sound. I am certain that JAMA would not have published a case-control study on the same topic that reported an odds ratio of 1.12, since this study design is much more susceptible to bias. You ask in your article on the air pollution study why anyone is concerned with a 12% increase in risk (assuming that is the correct number). The answer is that a nearly ubiquitous exposure which increases the risk of a disease slightly can impact far more people than can a very rare exposure which vastly increases disease risk. This should be apparent if you play around with the formula for population attributable risk.

Third, you should know (and should let your readers know) that epidemiologists themselves share many of your concerns. We recognize that individual epidemiologists have an incentive to overstate the importance of findings from their particular studies. The peer review and editorial process mitigates some of this tendency, though of course the extent to the process works depends on the journal. Thankfully, epidemiologic studies are published in a wide variety of journals dedicated to particular disease areas or more generally to epidemiology, and not just in NEJM and JAMA.

Lastly, epidemiologists are also concerned about data dredging. Your readers might be interested to know that so-called data dredging did not come about because of the sudden desire of epidemiologists to implicate everything under the sun as a risk factor. Rather, it followed the advent of the computational power to perform multiple regression with ease. I am among those who think that there is little wrong with data-dredging per se when used for hypothesis generation. It is only the reporting of such results as conclusions rather than as leads that is problematic.

Though we epidemiologists are a very critical bunch, occasionally an article with a sensationalistic spin will slip through the cracks. Please don’t let that sour you on an entire field.

That was in February. How did Murray respond to being corrected by an epidemiologist? Well, in March he wrote the other article where he said:

Epidemiologists generally agree that one cannot ascribe medical causation to a risk factor if the factor is associated with less than double the occurrence than normal.

He’d just been told by an epidemiologist that epidemiologists did not agree with that statement but he immediately turned around and wrote that they did. Now Murray himself might agree with the statement, but that’s not what he wrote. He wrote that epidemiologists generally agree with the statement, and that is something that Murray knew to be false.

John Quiggin has an interesting post putting the disinformation peddled by folks like Steve Milloy and Iain Murray in a broader context:

But at some point, it must be necessary to abandon the case-by-case approach and adopt a summary judgement about people like Milloy and sites like TCS. Nothing they say can be trusted. Even if you can check their factual claims (by no means always the case) it’s a safe bet that they’ve failed to mention relevant information that would undermine their case. So unless you have expert knowledge of the topic in question, they’re misleading, and if you have the knowledge, they’re redundant.

Of course, there’s nothing surprising about paid lobbyists twisting the truth. What’s more disturbing is the fact that the same approach dominates the Bush Administration. Admittedly, governments have never had a perfectly pure approach to science, but the distortion of the process under Bush is unparalleled, to the extent that it has produced unprecedented protests from the scientific community. Natural scientists aren’t alone in this. Economists, social scientists and even military and intelligence experts are horrified by the way in which processes that are supposed to produce expert advice have been politicised.

John Quiggin has another post on the right wing attack on science, this time describing the Australian front. Chris Mooney has great article in the The American Prospect about James Inhofe’s part in the attack on science.

And Iain Murray is at it again. He has a post where he refers to graph on the left, saying that it is one of the most important elements in the debate, and writing:

“The fact that the ten hottest years happened since 1991 may well be an artifact of the collapse in the number of weather monitoring stations contributing to the global temperature calculations following the fall of communism (see graph).”

As I’ve said before, I’m reluctant to comment on global warming because many others are better informed on the matter, but in the case of Murray’s graph, helps me. Even though I’m not an expert, it took me all of ten seconds to think of way to test to see if the increase was an artifact of the change in the weather stations reporting. All you have to is produce another graph of average temperature just using the weather stations that have data for the whole period. If this graph shows a similar increase, then Murray’s suggestion is proven false. If it doesn’t show an increase, then Murray’s suggestion is proven true. And if you have the data to produce this graph, then you have the data to produce the graph that tests his suggestion.

There are three possibilities:

1. Murray didn’t think of this really obvious test. In this case he isn’t competent to write about global warming.
2. The test was done and Murray knows that it showed that his suggestion was false. In this case it would not be honest for him to present his suggestion the way he did.
3. The test was done and Murray knows that it showed that his suggestion was true. If this was this case, why wouldn’t he say so?

Update: In comments, Christopher Enckell provides the source of the graph Murray showed: a paper by Ross McKitrick. McKitrick writes:

Figure 3 shows the total number of stations in the GHCN and the raw (arithmetic) average of temperatures for those stations. Notice that at the same time as the number of stations takes a dive (around 1990) the average temperature (red bars) jumps. This is due, at least in part, to the disproportionate loss of stations in remote and rural locations, as opposed to places like airports and urban areas where it gets warmer over time because of the build-up of the urban environment. This poses a problem for users of the data. Someone has to come up with an algorithm for deciding how much of the change in average temperature post-1990 is due to an actual change in the climate and how much is due to the change in the sample. When we hear over and over about records being set after 1990 in observed global temperatures this might mean the climate has changed, or it means an inadequate adjustment is being used, and there is no formal way to decide between these.

I’m stunned. As I wrote above, it took me ten seconds to think of way to test if the increase was due to a change in the sample and McKitrick writes that “there is no formal way to decide”. It would appear that my possibility 1 applies to both Murray and McKitrick.

Mrs M. Ingram
Old Dubbo
Dubbo
N.S.W.
Australia

To Dear Mother from Bob with best wishes. I hope you will like my beautiful costume. It is not a very costly robe. Send in your order without delay we are nearly sold out.

May 18, 1918
MIXED BATHING
PHOTO TAKEN AT BRIGTON ENGLAND

Left to Right: Pte Moore, Lance Corporal Cook, Pte Ingram, Private Ferguson in foreground watching the bathers. Excuse my grin, Fergy was tickling my leg as you can see by photo.

I recon I can pass the M.Ps. in that disguise. All the “Beetlecrushers” send thier kindest regards to you all in Australia.

Brighton is about 50 miles from Victoria Station London and is one of England’s famous seaside resorts.

xxxx Bob Ingram
3rd Batt.

[Bob Ingram was my grandmother’s oldest brother. Her father died when she was young, so he was like a father to her. After this picture was taken he was gassed on the western front. He was 19. He survived the gassing but died from his injuries a few years later. Lest we forget. TDL]

[This correspondence started with an email from McKitrick commenting on this post. I’ve edited it to remove most of the quoted text from previous emails. Further discussion is here.] (more…)

The graph above, which Iain Murray claimed showed that

“The fact that the ten hottest years happened since 1991 may well be an artifact of the collapse in the number of weather monitoring stations contributing to the global temperature calculations following the fall of communism (see graph)”

comes from this paper by Ross McKitrick. McKitrick recently was in the news for publishing a controversial paper that claimed that an “audit” of the commonly accepted reconstruction of temperatures over the past 1000 years was incorrect, so I thought it would be interesting to “audit” McKitrick’s graph.

I should first caution readers that I am not an expert in this area—I’m a computer scientist, not a climatologist. In other words, I’m no better qualified to comment on this than McKitrick. McKitrick writes:

“The main problem in the debate over what the Global Temperature is doing is that there is no such thing as a Global Temperature. Temperature is a continuous field, not a scalar, and there is no physics to guide reducing this field to a scalar, by averaging or any other method. Consequently the common practice of climate measurement is an ad hoc approximation of a non-existent quantity.”

This is untrue. Average temperature has a real, physical meaning. For example, if I have one kg of water at 20 degrees and another at 30 degrees, then their average temperature is 25 degrees. This is the temperature I would get if I mixed the water.

McKitrick then reproduces this graph (figure 2) (from GISS), describing it as “NASA’s version of this simulacrum”. He claims that a decreases in the number of weather stations is “problematic”, writing:

“In the early 1990s, the collapse of the Soviet Union and the budget cuts in many OECD economies led to a sudden sharp drop in the number of active weather stations.”

However, the graph he reproduces that shows the drop gives a different reason:

“The reasons why the number of stations in GHCN drop off in recent years are because some of GHCN’s source datasets are retroactive data compilations (e.g. World Weather Records) and other data were created or exchanged years ago.”

I looked at the GHCN data and while the number of weather stations in the former Soviet Union did drop from about 270 to 100, but the total number fell from 5000 to 2700 so the decrease there was only a small factor in the overall decrease.

McKitrick next refers to his figure at the top of this post:

“Figure 3 shows the total number of stations in the GHCN and the raw (arithmetic) average of temperatures for those stations. Notice that at the same time as the number of stations takes a dive (around 1990) the average temperature (red bars) jumps. This is due, at least in part, to the disproportionate loss of stations in remote and rural locations, as opposed to places like airports and urban areas where it gets warmer over time because of the build-up of the urban environment.”

I downloaded the raw GHCN temperature data from here, and tried to reproduce McKittrick’s graph by plotting the number of stations and the average temperature of all stations for each year. If you want to check my work, the program I wrote to do the calculations can be downloaded here. The graph above is reasonably similar to McKitrick’s graph. The biggest difference is that the right-hand vertical scale in McKittrick’s graph is clearly incorrect. The number peaked at 6,000, not 14,000 as his figure 3 indicates. (He actually has the correct number in his figure 2, which was copied from another paper.) Just taking the average of all the station temperatures is a rather poor way to estimate the global average temperature, since regions with a large number of stations will count for far too much in the global average. However, even this crude way of computing the average shows significant warming in the 90s. McKitrick’s graph is also rather misleading since the GISS graph above is not calculated this way—the stations are weighted so that regions get the correct weighting.

To test McKittrick’s claim that the warming in 90’s might have been caused by the decline in the number of stations, all I had to do was just consider the stations that has measurements for every year from 1980 to 2000. The average temperature of those stations is shown as the green line in the graph above, while the average of all stations is in red. The blue line is the average temperature shown in the GISS graph. Note that all three lines show significant warming in the 90s. Whether you analyse the data in a crude way or a sophisticated way you still see warming. It is true that after correcting for the change in the number of stations, the warming is less, but it actually agrees better with the average temperature shown in the GISS graph. If you look at Hansen et al’s paper

that describes how the GISS graph was constructed, you will find that of course they noticed and accounted for the change in the number of stations:

“Sampling studies discussed below indicate that the decline in number of stations is unimportant in regions of dense coverage, although the estimated global temperature change can be affected by a few hundredths of a degree.”

McKitrick does not acknowledge this or cite this paper.

The outcome of my analysis was just as I expected—if correcting for the change in the number of stations had removed the warming trend, Murray and McKitrick would already have told us about it.

In an email, McKitrick claimed that there were two problems with my test:

First, there was a change post-1990 in the quality of data in stations still operating, as well as the number of stations. Especially in the former Soviet countries after 1990, the rate of missing monthly records rose dramatically. So you need a subset of stations operating continuously and with reasonably continuous quality control.

However, the Soviet stations are only a small percentage of the total, so don’t make much difference. And of course, if you look at Hansen et al you find that they have extensive checks on the data quality.

McKitrick continued:

Second, if in this subset you observe an upward trend comparable to the conventional global average, in order to prove that this validates the global average you have to argue that the subset is a randomly chosen, representative sample of the whole Earth. Of course if this were true the temperature people would only use the continuously-available subset for their data products. It isn’t, which is why they don’t. It would leave you with a sample biased towards US and European cities, so it is not representative of the world as a whole. The large loss in the number of stations operating (50% in a few years) was not random in a geophysical sense, it was triggered by economic events, in which stations were closed in part if they were relatively costly to operate or if the country experienced a sudden loss of public sector resources. One can conjecture what the effect of that discontinuity was, but to test the conjecture, at some point you have to guess at what the unavailable data would have said if they were available. Because of that, I cannot see how one can devise a formal test of the representativeness of the subsample.

Now this is just wrong. You don’t need a random sample to estimate the temperature across the Earth’s surface. Temperatures tend to be quite similar at places that are close to each other. You just need to space your stations over the Earth’s surface and you have a representative sample. So you can actually estimate what the temperature would have been in the missing stations and you can actually test to see how representative the sample is and in fact Hansen et al wrote:

Sampling studies discussed below indicate that the decline in number of stations is unimportant in regions of dense coverage, although the estimated global temperature change can be affected by a few hundredths of a degree.

McKitrick, however, did not cite this paper.

McKitrick concludes:

None of this means that those researchers with access to the raw data can’t propose and implement such tests as you propose (I wish they would).

Gee, McKitrick implies that researchers hadn’t done such tests, when, as we have already seen, they had done such tests. When I challenged him on this, he contradicted himself:

I do not claim that adjustments are not being made, only that there is no formal test of their adequacy.

Presumably he talks of “formal” tests so he doesn’t have to count the tests that have actually been done. (Our entire email exchange is here.)

Chris Mooney notes that McKitrick defended Inhofe’s claim that “manmade global warming is the greatest hoax ever perpetrated”

I’m not the only one who has found problems with McKitrick’s writings on climate. Robert Grumbine has some comments on another McKitrick paper:

He was fooling around with correlating per capita income with the observed temperature changes. He concluded that the warming was a figment of climatologists imaginations, as there was a correlation between money and warming. ‘Obviously’ this had to be due to wealth creating the warming in the dataset, rather than any climate change—his conclusion.

Along the way he:

1. selected a subset of temperature records
   1. without using a random method
   2. without paying attention to spatial distribution
   3. without ensuring that the records were far enough apart to be independant—ok, I shouldn’t say ‘he’ did it, because he didn’t. He blindly took a selection that his student made and which was—to my eyes—distributed quite peculiarly.
2. Treated the records as being independant (I know William knows this, but for some other folks: Surface temperature records are correlated across fairly substantial distances—a few hundred km. This is what makes paleoreconstructions possible, and what makes it possible to initialize global numerical weather prediction models with so few observations.)
3. Ignored that we do expect, and have reason to expect that the warming will be higher in higher latitudes
4. Ignored that the wealthy countries are at higher latitudes

Hence my calling it fooling around rather than work or study. He was, he said, submitting that pile of tripe* to a journal.
*pile of tripe being my term, not his.

and

His main conclusion was regarding climate change—namely that there isn’t any. His secondary conclusion was that climate people studying climate data were idiots. Neither of those is a statement of economics, so my knowledge of economics is irrelevant (though, in matter of fact, it is far greater than his knowledge of climate; this says little, as his displayed level doesn’t challenge a bright jr. high student.).

Grumbine’s correspondence with McKitrick is here