I’ve always heard of the Metropolis algorithm having been invented for H-bomb calculations by Nicholas Metropolis and Edward Teller. But I was just looking at the original paper, and discovered that there are five authors: Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller. Particularly striking having two repeated surnames, and a bit of research uncovers that these were two married couples: Arianna Rosenbluth and Marshall Rosenbluth, and Augusta Teller and Edward Teller. In particular, Arianna Rosenbluth (née Wright) appears to have been a formidable character, according to her Wikipedia page: She completed her physics PhD at Harvard at the age of 22.
In keeping with the 1950s conception of computer programming as women’s work, the two women were responsible, in particular, for all the programming — a heroic undertaking in those pre-programming language days, on the MANIAC I — and Rosenbluth in particular did all the programming for the final paper.
And also in keeping with the expectations of the time, and more depressingly, according to the Wikipedia article “After the birth of her first child, Arianna left research to focus on raising her family.”
Everyone knows about the famous Dewey Defeats Truman headline fiasco, and that the Chicago Daily Tribune was inspired to its premature announcement by erroneous pre-election polls. But why were the polls so wrong?
The Social Science Research Council set up a committee to investigate the polling failure. Their report, published in 1949, listed a number of faults, including disparaging the very notion of trying to predict the outcome of a close election. But one important methodological criticism — and the one that significantly influenced the later development of political polling, and became the primary lesson in statistics textbooks — was the critique of quota sampling. (An accessible summary of lessons from the 1948 polling fiasco by the renowned psychologist Rensis Likert was published just a month after the election in Scientific American.)
Serious polling at the time was divided between two general methodologies: random sampling and quota sampling. Random sampling, as the name implies, works by attempting to select from the population of potential voters entirely at random, with each voter equally likely to be selected. This was still considered too theoretically novel to be widely used, whereas quota sampling had been established by Gallup since the mid-1930s. In quota sampling the voting population is modelled by demographic characteristics, based on census data, and each interviewer is assigned a quota to fill of respondents in each category: 51 women and 49 men, say, a certain number in the age range 21-34, or specific numbers in each “economic class” — of which Roper, for example, had five, one of which in the 1940s was “Negro”. The interviewers were allowed great latitude in filling their quotas, finding people at home or on the street.
In a sense, we have returned to quota sampling, in the more sophisticated version of “weighted probability sampling”. Since hardly anyone responds to a survey — response rates are typically no more than about 5% — there’s no way the people who do respond can be representative of the whole population. So pollsters model the population — or the supposed voting population — and reweight the responses they do get proportionately, according to demographic characteristics. If Black women over age 50 are thought to be equally common in the voting population as white men under age 30, but we have twice as many of the former as the latter, we count the responses of the latter twice as much as the former in the final estimates. It’s just a way of making a quota sample after the fact, without the stress of specifically looking for representatives of particular demographic groups.
Consequently, it has most of the deficiencies of a quota sample. The difficulty of modelling the electorate is one that has gotten quite a bit of attention in the modern context: We know fairly precisely how demographic groups are distributed in the population, but we can only theorise about how they will be distributed among voters at the next election. At the same time, it is straightforward to construct these theories, to describe them, and to test them after the fact. The more serious problem — and the one that was emphasised in the commission report in 1948, but has been less emphasised recently — is in the nature of how the quotas are filled. The reason for probability sampling is that taking whichever respondents are easiest to get — a “sample of convenience” — is sure to give you a biased sample. If you sample people from telephone directories in 1936 then it’s easy to see how they end up biased against the favoured candidate of the poor. If you take a sample of convenience within a small demographic group, such as middle-income people, then it won’t be easy to recognise how the sample is biased, but it may still be biased.
For whatever reason, in the 1930s and 1940s, within each demographic group the Republicans were easier for the interviewers to contact than the Democrats. Maybe they were just culturally more like the interviewers, so easier for them to walk up to on the street. And it may very well be that within each demographic group today Democrats are more likely to respond to a poll than Republicans. And if there is such an effect, it’s hard to correct for it, except by simply discounting Democrats by a certain factor based on past experience. (In fact, these effects can be measured in polling fluctuations, where events in the news lead one side or the other to feel discouraged, and to be less likely to respond to the polls. Studies have suggested that this effect explains much of the short-term fluctuation in election polls during a campaign.)
Interestingly, one of the problems that the commission found with the 1948 polling with relevance for the Trump era was the failure to consider education as a significant demographic variable.
All of the major polling organizations interviewed more people with college education than the actual proportion in the adult population over 21 and too few people with grade school education only.
And then I see the disinfectant, where it knocks it out in a minute. One minute! And is there a way we can do something like that, by injection inside or almost a cleaning. Because you see it gets in the lungs and it does a tremendous number on the lungs. So it would be interesting to check that.
When Donald Trump used a Covid-19 press briefing to recommend injecting disinfectants to kill viruses within the human body, people reacted as though this were entirely unprecedented. But it wasn’t, entirely. From Frank Snowden’s Epidemics and Society:
Of all nineteenth-century treatments for epidemic cholera, however, perhaps the most painful was the acid enema, which physicians administered in the 1880s in a burst of excessive optimism after Robert Koch’s discovery of V. cholerae. Optimistic doctors reasoned that since they at last knew what the enemy was and where it was lodged in the body, and since they also understood that bacteria are vulnerable to acid, as Lister had demonstrated, all they needed to destroy the invader and restore patients’ health was to suffuse their bowels with carbolic acid. Even though neither Koch nor Lister ever sanctioned such a procedure, some of their Italian followers nevertheless attempted this treatment during the epidemic of 1884–1885. The acid enema was an experimental intervention that, in their view, followed the logic of Koch’s discoveries and Lister’s practice. The results, however, were maximally discouraging…
Apparently it’s a not uncommon response on someone first learning of the germ theory of disease.
A little-publicised development in statistics over the past two decades has been the admission of causality into respectable statistical discourse, spearheaded by the computer scientist Judea Pearl. Pearl’s definition (joint with Joseph Harpern) of causation (“X having setting x caused effect E”) has been formulated approximately as follows:
X=x and E occurs.
But for the fact that X=x, E would not have occurred.
Of course, Pearl is not the first person to think carefully about causality. He would certainly recognise the similarity to Koch’s postulates on demonstrating disease causation by a candidate microbe:
No disease without presence of the organism;
The organism must be isolated from a host containing the disease ;
The disease must arise when the organism is introduced into a healthy animal;
The organism isolated from that animal must be identified as the same original organism.
I was reminded of this recently in reading the Buddhist Assutava Sutta, the discourse on “dependent co-arising”, where this formula (that also appears in very similar wording in a wide range of other Buddhist texts) is stated:
If I had been asked when it first came to be understood that skin cancer is caused by exposure to the sun, I would have said probably the 1970s, maybe 1960s among cognoscenti, before it was well enough established to become part of public health campaigns. But I was just reading this 1953 article by C. O. Nordling on mutations and cancer — proposing, interestingly enough, that cancers are caused by the accumulation of about seven mutations in a cell — which mentions, wholly incidentally, in a discussion of latency periods between the inception of a tumour cell and disease diagnosis
40 years for seaman’s cancer (caused by solar radiation).
So, apparently skin cancer was known to be frequent among sailors, and the link to sun exposure was sufficiently well accepted to be mentioned here parenthetically.
Reading Dava Sobel’s book on the women astronomers of the Harvard Observatory in the early 20th century, The Glass Universe, I was surprised to discover that the first Association to Aid Scientific Research by Women was founded in the 19th century. It awarded grants and an Ellen Richards Research prize, named for the first woman admitted to MIT, who went on to become associate professor of chemistry at MIT, while remaining unpaid. The prize was last awarded in 1932. Why?
[After selecting the winners of the 1932 prize] the twelve members declared themselves satisfied with the progress they had seen, and they drafted a resolution to dissolve the organization. “Whereas,” it said, “the objects for which this association has worked for thirty-five years have been achieved, since women are given opportunities in Scientific Research on an equality with men, and to gain recognition for their achievements, be it Resolved, that this association cease to exist after the adjournment of this meeting.”
I just listened to all of a two-hour discussion between journalist Ezra Klein and professional atheist Sam Harris, about Harris’s defense of the right-wing policy entrepreneur (as Matthew Yglesias has described him) Charles Murray, famous for his racist application of intelligence research to public policy, most famously in a notorious chapter of his book The Bell Curve. Klein pushes back effectively against Harris’s self-serving martyrdom — Harris, not unreasonably, identifies with the suffering of a wealthy and famous purveyor of quack science whose livelihood is ever-so-slightly harmed by public criticism* — but he doesn’t sufficiently engage, I think, with Harris’s contention that he is promoting the values of real science. Unfortunately, the “mainstream social science” that Harris and Murray are promoting exists only in secret messages from “reputable scientists in my inbox, who have totally taken my side in this, but who are too afraid to say so publicly”. Harris doesn’t allow for a second that there is any good-faith argument on the other side. Anyone who disagrees is merely trying to shut down scientific progress, or simply confusing scientific truth with do-gooding wishful thinking.
The truth of the matter is, Murray and other brave seekers of truth are doing the opposite of helping to clarify reality. They are wading into a swamp of confusion, and pulling out some especially stinky slime that they can hurl at disfavoured groups.
As much as Harris tries to promote Murray as a pure-hearted “content-of-our-character” anti-racist individualist, as long as “race” exists as a social factor affecting people’s self-image, the communities they belong to, and the way they are perceived by others, it remains a potent social force. When demographers argue that “race” isn’t “real”, they are saying that racial categories don’t separate natural clusters by genetic or physical traits. When Murray says, let’s stop talking about race, let’s talk about individual genetic endowments, he is saying that racial groupings have no causal effect on their own, but only label clusters whose difference arise from deep physical causes — wrong on both sides. Continue reading “Neanderthal science”
In the past I’ve read a few individual individual essays by Montaigne, but lately I’ve been really enjoying reading them systematically — partly listening to the English-language audiobook, partly reading the lovely annotated French edition by Jean Céard et al. It’s fascinating to see the blend of inaccessibly foreign worldview with ideas that seem at times astoundingly modern. For example, in the essay titled “On the resemblence of children to their fathers” (which seems to have almost nothing at all to say about the resemblence of children to their fathers), in the course of disparaging contemporary medicine Montaigne suddenly anticipates the need for random controlled trials — while at the same time despairing of such a daunting intellectual project. After acknowledging a few minor cases in which physicians seem to have learned something from experience he continues
Mais en la plus part des autres experiences, à quoy ils disent avoir esté conduis par la fortune, et n’avoir eu autre guide que le hazard, je trouve le progrez de cette information incroyable. J’imagine l’homme, regardant au tour de luy le nombre infiny des choses, plantes, animaux, metaulx. Je ne sçay par où luy faire commencer son essay : et quand sa premiere fantasie se jettera sur la corne d’un elan, à quoy il faut prester une creance bien molle et aisée : il se trouve encore autant empesché en sa seconde operation. Il luy est proposé tant de maladies, et tant de circonstances, qu’avant qu’il soit venu à la certitude de ce poinct, où doit joindre la perfection de son experience, le sens humain y perd son Latin : et avant qu’il ait trouvé parmy cette infinité de choses, que c’est cette corne : parmy cette infinité de maladies, l’epilepsie : tant de complexions, au melancholique : tant de saisons, en hyver : tant de nations, au François : tant d’aages, en la vieillesse : tant de mutations celestes, en la conjonction de Venus et de Saturne : tant de parties du corps au doigt. A tout cela n’estant guidé ny d’argument, ny de conjecture, ny d’exemple, ny d’inspiration divine, ains du seul mouvement de la fortune, il faudroit que ce fust par une fortune, parfaictement artificielle, reglée et methodique Et puis, quand la guerison fut faicte, comment se peut il asseurer, que ce ne fust, que le mal estoit arrivé à sa periode ; ou un effect du hazard ? ou l’operation de quelque autre chose, qu’il eust ou mangé, ou beu, ou touché ce jour là ? ou le merite des prieres de sa mere-grand ? Davantage, quand cette preuve auroit esté parfaicte, combien de fois fut elle reiterée ? et cette longue cordée de fortunes et de rencontres, r’enfilée, pour en conclure une regle.
But in most other experiences, where they claim to have been led by accidents, having no other guide than chance, I find the progress of this information hard to believe. I imagine a man looking about him at the infinite number of things, plants, animals, metals. I don’t where he would start. And when his first whim took him to an elk horn, which might be easy to believe in, he would find his second step blocked: There are so many diseases, so many individual circumstances, that before he could arrive at any certainty on this point, he will have arrived at the end of human sense: before he could find, among this infinity of things, that it is this horn; among the infinity of diseases, epilepsy; among the individual conditions, the melancholic temperament; among all the ages, the elderly; among all the astrological conditions, the conjunction of Venus and Saturn; among all the parts of the body, the finger. And all of this, being led by no argument, by no prior examples, by no divine inspiration, but purely by chance, it must be achieved by the most completely artificial, methodical and regulated turn of chance. And suppose the cure has been accomplished, how could you tell whether the disease might not have simply run its course, or the improvement occurred purely by chance? Or if it might not have been the effect of some other factor, something he ate, or drank, or touched on that day? Or the merit of his grandmother’s prayers? And if you could provide complete proof in one case, how many times would you need to repeat the trial, and this long series of random encounters, before you could conclusively determine the rule.
I was reading Sharon McGrayne’s wonderful popular (no, really!) book on the history of Bayesian statistics. At one point it is mentioned that George Box wrote a song for a departmental Christmas party
There’s no theorem like Bayes’ Theorem
Like no theorem I know…
A bit later we read of Howard Raiffa and Robert Schlaifer singing
Anything frequentists can do, Bayesians do better
(More or less… the exact text is not reproduced.) So it seems the underappreciated role of Irving Berlin in the development of Bayesian thought has yet to be adumbrated. Perhaps researchers will some day uncover such hits manqués as “How High is the Bayes Factor?”, “I’m Dreaming of a Conjugate Prior”, or even “Bayes Bless America”.
I’ve just been reading Amir Alexander’s book Infinitesimal, about the intellectual struggle over the concepts of infinitesimals and the continuum in mathematics and science (and theology) in the 17th century. The early part of the book is a history of the Society of Jesus, presented as a ruthless and intellectually daring force for religious conservatism, strictly hierarchical, devoted to its holy founder, a thoroughly mystical movement that built its reputation and influence on educational outreach. And then it struck me: The Jesuits were just like Chabad-Lubavitch!