Author Archives: Andrew

“I was left with an overwhelming feeling that the World Values Survey is simply a vehicle for telling stories about values . . .”

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Dale Lehman writes:

My guess is that you are familiar with the World Values Survey – I was not until I saw it described in the Economist this week (August 12, 2023). It has probably been used in the careers of many academics and is a monumental effort to collect survey data about values from across the world over a long period of time (the latest wave of the survey includes around 130,000 respondents from at least 90 countries). With the caveat that I have no experience with this data and have not read anything about its methodological development, I am struck by what seems like a shoddy ill-conceived research effort. To begin with a minor thing that appeared in the Economist story, I’ve attached a screenshot of part of what appears in the print magazine (the online version interactively builds up this view so the print version is more complete but provides less context). I have an issue with the visualization – some might call it a quibble, I’d call it a major problem – and I can’t tell if the blame lies with The Economist or the WVS, but it is what first alerted me to this data. The change in values over time is shown by the line segments ending in a circle marker for the latest survey wave. Why didn’t they use arrows rather than a circle at one end? I think this is inexcusable – arrows invoke pre-attentive visual processing whereas the line segment/circles force me to constantly reassess the picture to understand how things are changing. In other words, the visual presented doesn’t work – arrows would be immensely better. I don’t believe that is just sloppiness – I think it reveals something more fundamental, and that is what really concerns me about the WVS.

Moving on in the graph, I am immediately stuck by the dimensions of the graph. The methodology is described in detail on the WVS website (https://www.worldvaluessurvey.org/WVSContents.jsp) and I haven’t reviewed it in detail. But I have a number of issues about these measurements. Among these:

– The survival-self expression dimensions strikes me as unintuitive. Since the questions involved (such as the importance of religion vs the importance of environmental protection) are linked to wealth, and much of the WVS research concerns changes in values as wealth changes, why not measure wealth directly? My preference would be for the more unambiguous (relatively speaking) measures like GDP than these derived measures that seem vague to me.

– I have similar issues with the other dimension: traditional vs secular-rational. Neither of these seem intuitive to me and the underlying questions don’t improve things. There are questions about “national pride” and self descriptions of whether or not someone feels “very happy.” I find it very difficult to see how these map cleanly into the dimension they are being used for.

– Since these surveys are done across many countries and over time, I think the meaning of the words may change. For example, asking whether “people are trustworthy” requires the idea of “trust” to mean the same thing in different places and different periods of time. I see no evidence of this and can imagine that there might be differences in how people interpret phrases like that. In general, it seems to me that the wording of these survey questions was not carefully thought out or tested (though perhaps I just am not familiar with their development).

– I am disturbed by the use of single points to represent entire countries. Indeed, there is considerable discussion of how heterogeneous countries are, but the graphs use average measures to represent entire countries. As with many things, the average may be less interesting than the variability. This concern is accentuated by the aggregation of these countries into groups such as “Protestant Europe” and “Orthodox Europe.” I don’t find these groups particularly intuitive either.

– I’m unconvinced that the two dimensional picture of values is the best way to analyze values. Are there two dimensions the most important? Why two? Perhaps the changes over time simply reveal how valid the dimensions are rather than any intrinsic changes in values people hold.

There is more, but I’ll stress again that I have no background with this data. I can say it was difficult for me to even read the Economist article since almost every statement struck me as troublesome regarding what was being measured and how it relates to the fundamental methodology of this two dimensional view of values. I also can’t tell how much of my concern lies with the Economist article or the WVS itself. But I was left with an overwhelming feeling that the WVS is simply a vehicle for telling stories about values and how they differ between countries or groups of people and how these change over time. Those stories are naturally interesting, but I don’t see that the methodology and data support any particular story over any other. It seems like a perfect mechanism for academic career development, but little else.

My reply: I’m not sure! I’ve never worked with the World Values Survey myself. Maybe some readers can share their thoughts?

Inspiring story from a chemistry classroom

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From former chemistry teacher HildaRuth Beaumont:

I was reminded of my days as a newly qualified teacher at a Leicestershire comprehensive school in the 1970s, when I was given a group of reluctant pupils with the instruction to ‘keep them occupied’. After a couple of false starts we agreed that they might enjoy making simple glass ornaments. I knew a little about glass blowing so I was able to teach them how to combine coloured and transparent glass to make animal figures and Christmas tree decorations. Then one of them made a small bottle complete with stopper. Her classmate said she should buy some perfume, pour some of it into the bottle and give it to her mum as a Mother’s Day gift. ‘We could actually make the perfume too,’ I said. With some dried lavender, rose petals, and orange and lemon peel, we applied solvent extraction and steam distillation to good effect and everyone was able to produce small bottles of perfume for their mothers.

What a wonderful story. We didn’t do anything like this in our high school chemistry classes! Chemistry 1 was taught by an idiot who couldn’t understand the book he was teaching out of. Chemistry 2 was taught with a single-minded goal of teaching us how to solve the problems on the Advanced Placement exam. We did well on the exam and learned essentially zero chemistry. On the plus side, this allowed me to place out of the chemistry requirement in college. On the minus side . . . maybe it would’ve been good for me to learn some chemistry in college. I don’t remember doing any labs in Chemistry 2 at all!

Preregistration is a floor, not a ceiling.

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This comes up from time to time, for example someone sent me an email expressing a concern that preregistration stifles innovation: if Fleming had preregistered his study, he never would’ve noticed the penicillin mold, etc.

My response is that preregistration is a floor, not a ceiling. Preregistration is a list of things you plan to do, that’s all. Preregistration does not stop you from doing more. If Fleming had followed a pre-analysis protocol, that would’ve been fine: there would have been nothing stopping him from continuing to look at his bacterial cultures.

As I wrote in comments to my 2022 post, “What’s the difference between Derek Jeter and preregistration?” (which I just added to the lexicon), you don’t preregister “the” exact model specification; you preregister “an” exact model specification, and you’re always free to fit other models once you’ve seen the data.

It can be really valuable to preregister, to formulate hypotheses and simulate fake data before gathering any real data. To do this requires assumptions—it takes work!—and I think it’s work that’s well spent. And then, when the data arrive, do everything you’d planned to do, along with whatever else you want to do.

Planning ahead should not get in the way of creativity. It should enhance creativity because you can focus your data-analytic efforts on new ideas rather than having to first figure out what defensible default thing you’re supposed to do.

Aaaand, pixels are free, so here’s that 2002 post in full:
Continue reading →

“On the uses and abuses of regression models: a call for reform of statistical practice and teaching”: We’d appreciate your comments . . .

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John Carlin writes:

I wanted to draw your attention to a paper that I’ve just published as a preprint: On the uses and abuses of regression models: a call for reform of statistical practice and teaching (pending publication I hope in a biostat journal). You and I have discussed how to teach regression on a few occasions over the years, but I think with the help of my brilliant colleague Margarita Moreno-Betancur I have finally figured out where the main problems lie – and why a radical rethink is needed. Here is the abstract:

When students and users of statistical methods first learn about regression analysis there is an emphasis on the technical details of models and estimation methods that invariably runs ahead of the purposes for which these models might be used. More broadly, statistics is widely understood to provide a body of techniques for “modelling data”, underpinned by what we describe as the “true model myth”, according to which the task of the statistician/data analyst is to build a model that closely approximates the true data generating process. By way of our own historical examples and a brief review of mainstream clinical research journals, we describe how this perspective leads to a range of problems in the application of regression methods, including misguided “adjustment” for covariates, misinterpretation of regression coefficients and the widespread fitting of regression models without a clear purpose. We then outline an alternative approach to the teaching and application of regression methods, which begins by focussing on clear definition of the substantive research question within one of three distinct types: descriptive, predictive, or causal. The simple univariable regression model may be introduced as a tool for description, while the development and application of multivariable regression models should proceed differently according to the type of question. Regression methods will no doubt remain central to statistical practice as they provide a powerful tool for representing variation in a response or outcome variable as a function of “input” variables, but their conceptualisation and usage should follow from the purpose at hand.

The paper is aimed at the biostat community, but I think the same issues apply very broadly at least across the non-physical sciences.

Interesting. I think this advice is roughly consistent with what Aki, Jennifer, and I say and do in our books Regression and Other Stories and Active Statistics.

More specifically, my take on teaching regression is similar to what Carlin and Moreno say, with the main difference being that I find that students have a lot of difficulty understanding plain old mathematical models. I spend a lot of time teaching the meaning of y = a + bx, how to graph it, etc. I feel that most regression textbooks focus too much on the error term and not enough on the deterministic part of the model. Also, I like what we say on the first page of Regression and Other Stories, about the three tasks of statistics being generalizing from sample to population, generalizing from control to treatment group, and generalizing from observed data to underlying constructs of interest. I think models are necessary for all three of these steps, so I do think that understanding models is important, and I’m not happy with minimalist treatments of regression that describe it as a way of estimating conditional expectations.

The first of these tasks is sampling inference, the second is causal inference, and the third refers to measurement. Statistics books (including my own) spend lots of time on sampling and causal inference, not so much on measurement. But measurement is important! For an example, see here.

If any of you have reactions to Carlin and Moreno’s paper, or if you have reactions to my reactions, please share them in comments, as I’m sure they’d appreciate it.

How often is there a political candidate such as Vivek Ramaswamy who is so much stronger in online polls than telephone polls?

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Palko points to this news article, “The mystery of Vivek Ramaswamy’s rapid rise in the polls,” which states:

Ramaswamy’s strength comes almost entirely from polls conducted over the internet, according to a POLITICO analysis. In internet surveys over the past month — the vast majority of which are conducted among panels of people who sign up ahead of time to complete polls, often for financial incentives — Ramaswamy earns an average of 7.8 percent, a clear third behind Trump and DeSantis.

In polls conducted mostly or partially over the telephone, in which people are contacted randomly, not only does Ramaswamy lag his average score — he’s way back in seventh place, at just 2.6 percent.

There’s no singular, obvious explanation for the disparity, but there are some leading theories for it, namely the demographic characteristics and internet literacy of Ramaswamy’s supporters, along with the complications of an overly white audience trying to pronounce the name of a son of immigrants from India over the phone.”

And then, in order for a respondent to choose Ramaswamy in a phone poll, he or she will have to repeat the name back to the interviewer. And the national Republican electorate is definitely older and whiter than the country as a whole: In a recent New York Times/Siena College poll, more than 80 percent of likely GOP primary voters were white, and 38 percent were 65 or older.

‘When your candidate is named Vivek Ramaswamy,’ said one Republican pollster, granted anonymity to discuss the polling dynamics candidate, ‘that’s like DEFCON 1 for confusion and mispronunciation.’

Palko writes:

Keeping in mind that the “surge” was never big (maxed out at 10% and has been flat since), we’re talking about fairly small numbers in absolute terms, here are some questions:

1. How much do we normally expect phone and online to agree?

2. Ramaswamy generally scores around 3 times higher online than with phone. Have we seen that magnitude before?

3. How about a difficult name bias. Have we seen that before? How about Buttigieg, for instance? Did a foreign-sounding name hurt Obama in early polls?

4. Is the difference in demographics great enough to explain the difference? Aren’t things like gender and age normally reweighted?

5. Are there other explanations we should consider?

I don’t have any answers here, just one thought which is that it’s early in the campaign (I guess I should call it the pre-campaign, given that the primary elections haven’t started yet), and so perhaps journalists are reasoning that, even if this candidate is not very popular among voters, his active internet presence makes him a reasonable dark-horse candidate looking forward. An elite taste now but could perhaps spread to the non-political-junkies in the future? Paradoxically, the fact that Ramaswamy has this strong online support despite his extreme political stances could be taken as a potential sign of strength? I don’t know.

“Hot hand”: The controversy that shouldn’t be. And thinking more about what makes something into a controversy:

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I was involved in a recent email discussion, leading to this summary:

There is no theoretical or empirical reason for the hot hand to be controversial. The only good reason for there being a controversy is that the mistaken paper by Gilovich et al. appeared first. At this point we should give Gilovich et al. credit for bringing up the hot hand as a subject of study and accept that they were wrong in their theory, empirics, and conclusions, and we can all move on. There is no shame in this for Gilovich et al. We all make mistakes, and what’s important is not the personalities but the research that leads to understanding, often through tortuous routes.

“No theoretical reason”: see discussion here, for example.

“No empirical reason”: see here and lots more in the recent literature.

“The only good reason . . . appeared first”: Beware the research incumbency rule.

More generally, what makes something a controversy? I’m not quite sure, but I think the news media play a big part. We talked about this recently in the context of the always-popular UFOs-as-space-aliens theory, which used to be considered a joke in polite company but now seems to have reached the level of controversy.

I don’t have anything systematic to say about all this right now, but the general topic seems very worthy of study.

“Here’s the Unsealed Report Showing How Harvard Concluded That a Dishonesty Expert Committed Misconduct”

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Stephanie Lee has the story:

Harvard Business School’s investigative report into the behavioral scientist Francesca Gino was made public this week, revealing extensive details about how the institution came to conclude that the professor committed research misconduct in a series of papers.

The nearly 1,300-page document was unsealed after a Tuesday ruling from a Massachusetts judge, the latest development in a $25 million lawsuit that Gino filed last year against Harvard University, the dean of the Harvard Business School, and three business-school professors who first notified Harvard of red flags in four of her papers. All four have been retracted. . . .

According to the report, dated March 7, 2023, one of Gino’s main defenses to the committee was that the perpetrator could have been someone else — someone who had access to her computer, online data-storage account, and/or data files.

Gino named a professor as the most likely suspect. The person’s name was redacted in the released report, but she is identified as a female professor who was a co-author of Gino’s on a 2012 now-retracted paper about inducing honest behavior by prompting people to sign a form at the top rather than at the bottom. . . . Allegedly, she was “angry” at Gino for “not sufficiently defending” one of their collaborators “against perceived attacks by another co-author” concerning an experiment in the paper.

But the investigation committee did not see a “plausible motive” for the other professor to have committed misconduct by falsifying Gino’s data. “Gino presented no evidence of any data falsification actions by actors with malicious intentions,” the committee wrote. . . .

Gino’s other main defense, according to the report: Honest errors may have occurred when her research assistants were coding, checking, or cleaning the data. . . .

Again, however, the committee wrote that “she does not provide any evidence of [research assistant] error that we find persuasive in explaining the major anomalies and discrepancies.”

The full report is at the link.

Some background is here, also here, and some reanalyses of the data are linked here.

Now we just have to get to the bottom of the story about the shredder and the 80-pound rock and we’ll pretty much have settled all the open questions in this field.

We’ve already determined that the “burly coolie” story and the “smallish town” story never happened.

It’s good we have dishonesty experts. There’s a lot of dishonesty out there.

Abraham Lincoln and confidence intervals

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This one from 2017 is good; I want to share it with all of you again:

Our recent discussion with mathematician Russ Lyons on confidence intervals reminded me of a famous logic paradox, in which equality is not as simple as it seems.

The classic example goes as follows: Abraham Lincoln is the 16th president of the United States, but this does not mean that one can substitute the two expressions “Abraham Lincoln” and “the 16th president of the United States” at will. For example, consider the statement, “If things had gone a bit differently in 1860, Stephen Douglas could have become the 16th president of the United States.” This becomes flat-out false if we do the substitution: “If things had gone a bit differently in 1860, Stephen Douglas could have become Abraham Lincoln.”

Now to confidence intervals. I agree with Rink Hoekstra, Richard Morey, Jeff Rouder, and Eric-Jan Wagenmakers that the following sort of statement, “We can be 95% confident that the true mean lies between 0.1 and 0.4,” is not in general a correct way to describe a classical confidence interval. Classical confidence intervals represent statements that are correct under repeated sampling based on some model; thus the correct statement (as we see it) is something like, “Under repeated sampling, the true mean will be inside the confidence interval 95% of the time” or even “Averaging over repeated samples, we can be 95% confident that the true mean lies between the lower and upper endpoints of the confidence interval.” Russ Lyons, however, felt the statement “We can be 95% confident that the true mean lies between 0.1 and 0.4,” was just fine. In his view, “this is the very meaning of “confidence.'”

This is where Abraham Lincoln comes in. We can all agree on the following summary:

A. Averaging over repeated samples, we can be 95% confident that the true mean lies between the lower and upper endpoints of the confidence interval.

And we could even perhaps feel that the phrase “confidence interval” implies “averaging over repeated samples,” and thus the following statement is reasonable:

B. “We can be 95% confident that the true mean lies between the lower and upper endpoints of the confidence interval.”

Now consider the other statement that caused so much trouble:

C. “We can be 95% confident that the true mean lies between 0.1 and 0.4.”

In a problem where the confidence interval is [0.1, 0.4], “the lower and upper endpoints of the confidence interval” is just “0.1 and 0.4.” So B and C are the same, no? No. Abraham Lincoln, meet the 16th president of the United States.

In statistical terms, once you supply numbers on the interval, you’re conditioning on it. You’re no longer implicitly averaging over repeated samples. Just as, once you supply a name to the president, you’re no longer implicitly averaging over possible elections.

So here’s what happened. We can all agree on statement A. Statement B is a briefer version of A, eliminating the explicit mention of replications because they are implicit in the reference to a confidence interval. Statement C does a seemingly innocuous switch but, as a result, implies conditioning on the interval, thus resulting in a much stronger statement that is not necessarily true (that is, in mathematical terms, is not in general true).

None of this is an argument over statistical practice. One might feel that classical confidence statements are a worthy goal for statistical procedures, or maybe not. But, like it or not, confidence statements are all about repeated sampling and are not in general true about any particular interval that you might see.

P.S. More here.

Their signal-to-noise ratio was low, so they decided to do a specification search, use a one-tailed test, and go with a p-value of 0.1.

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Adam Zelizer writes:

I saw your post about the underpowered COVID survey experiment on the blog and wondered if you’ve seen this paper, “Counter-stereotypical Messaging and Partisan Cues: Moving the Needle on Vaccines in a Polarized U.S.” It is written by a strong team of economists and political scientists and finds large positive effects of Trump pro-vaccine messaging on vaccine uptake.

They find large positive effects of the messaging (administered through Youtube ads) on the number of vaccines administered at the county level—over 100 new vaccinations in treated counties—but only after changing their specification from the prespecified one in the PAP. The p-value from the main modified specification is only 0.097, from a one-tailed test, and the effect size from the modified specification is 10 times larger than what they get from the pre-specified model. The prespecified model finds that showing the Trump advertisement increased the number of vaccines administered in the average treated county by 10; the specification in the paper, and reported in the abstract, estimates 103 more vaccines. So moving from the specification in the PAP to the one in the paper doesn’t just improve precision, but it dramatically increases the estimated treatment effect. A good example of suppression effects.

They explain their logic for using the modified specification, but it smells like the garden of forking paths.

Here’s a snippet from the article:

I don’t have much to say about the forking paths except to give my usual advice to fit all reasonable specifications and use a hierarchical model, or at the very least do a multiverse analysis. No reason to think that the effect of this treatment should be zero, and if you really care about effect size you want to avoid obvious sources of bias such as model selection.

The above bit about one-tailed tests reflects a common misunderstanding in social science. As I’ll keep saying until my lips bleed, effects are never zero. They’re large in some settings, small in others, sometimes positive, sometimes negative. From the perspective of the researchers, the idea of the hypothesis test is to give convincing evidence that the treatment truly has a positive average effect. That’s fine, and it’s addressed directly through estimation: the uncertainty interval gives you a sense of what the data can tell you here.

When they say they’re doing a one-tailed test and they’re cool with a p-value of 0.1 (that would be 0.2 when following the standard approach) because they have “low signal-to-noise ratios” . . . that’s just wack. Low signal-to-noise ratio implies high uncertainty in your conclusions. High uncertainty is fine! You can still recommend this policy be done in the midst of this uncertainty. After all, policymakers have to do something. To me, this one-sided testing and p-value thresholding thing just seems to be missing the point, in that it’s trying to squeeze out an expression of near-certainty from data that don’t admit such an interpretation.

P.S. I do not write this sort of post out of any sort of animosity toward the authors or toward their topic of research. I write about these methods issues because I care. Policy is important. I don’t think it is good for policy for researchers to use statistical methods that lead to overconfidence and inappropriate impressions of certainty or near-certainty. The goal of a statistical analysis should not be to attain statistical significance or to otherwise reach some sort of success point. It should be to learn what we can from our data and model, and to also get a sense of what we don’t know..

Zotero now features retraction notices

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David Singerman writes:

Like a lot of other humanities and social sciences people I use Zotero to keep track of citations, create bibliographies, and even take & store notes. I also am not alone in using it in teaching, making it a required tool for undergraduates in my classes so they learn to think about organizing their information early on. And it has sharing features too, so classes can create group bibliographies that they can keep using after the semester ends.

Anyway my desktop client for Zotero updated itself today and when it relaunched I had a big red banner informing me that an article in my library had been retracted! I didn’t recognize it at first, but eventually realized that was because it was an article one of my students had added to their group library for a project.

The developers did a good job of making the alert unmissable (i.e. not like a corrections notice in a journal), the full item page contains lots of information and helpful links about the retraction, and there’s a big red X next to the listing in my library. See attached screenshots.

The way they implemented it will also help the teaching component, since a student will get this alert too.

Singerman adds this P.S.:

This has reminded me that some time ago you posted something about David Byrne, and whatever you said, it made me think of David Byrne’s wonderful appearance on the Colbert Report.

What was amazing to me when I saw it was that it’s kind of like a battle between Byrne’s inherent weirdness and sincerity, and Colbert’s satirical right-wing bloviator character. Usually Colbert’s character was strong enough to defeat all comers, but . . . decide for yourself.

Putting a price on vaccine hesitancy (Bayesian analysis of a conjoint experiment)

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Tom Vladeck writes:

I thought you may be interested in some internal research my company did using a conjoint experiment, with analysis using Stan! The upshot is that we found that vaccine hesitant people would require a large payment to take the vaccine, and that there was a substantial difference between the prices required for J&J and Moderna & Pfizer (evidence that the pause was very damaging). You can see the model code here.

My reply: Cool! I recommend you remove the blank lines from your Stan code as that will make your program easier to read.

Vladeck responded:

I prefer a lot of vertical white space. But good to know that I’m likely in the minority there.

For me, it’s all about the real estate. White space can help code be more readable but it should be used sparingly. What I’d really like is a code editor that does half white spaces.

Refuted papers continue to be cited more than their failed replications: Can a new search engine be built that will fix this problem?

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Paul von Hippel writes:

Stuart Buck noticed your recent post on A WestLaw for Science. This is something that Stuart and I started talking about last year, and Stuart, who trained as an attorney, believes it was first suggested by a law professor about 15 years ago.

Since the 19th century, the legal profession has had citation indices that do far more than count citations and match keywords. Resources like Shepard’s Citations—first printed in 1873 and now published online along with competing tools such as JustCite, KeyCite, BCite, and SmartCite—do not just find relevant cases and statutes; they show lawyers whether a case or statute is still “good law.” Legal citation indexes show lawyers which cases have been affirmed or cited approvingly, and which have been criticized, reversed, or overruled by later courts.

Although Shepard’s Citations inspired the first Science Citation Index in 1960, which in turn inspired tools like Google Scholar, today’s academic search engine still rely primarily on citation counts and keywords. As a result, many scientists are like lawyers who walk into the courtroom unaware that a case central to their argument has been overruled.

Kind of, but not quite. A key difference is that in the courtroom there is some reasonable chance that the opposing lawyer or the judge will notice that the key case has been overruled, so that your argument that hinges on that case will fail. You have a clear incentive to not rely on overruled cases. In science, however, there’s no opposing lawyer and no judge: you can build an entire career on studies that fail to replicate, and no problem at all, as long as you don’t pull any really ridiculous stunts.

Hippel continues:

Let me share a couple of relevant articles that we recently published.

One, titled “Is Psychological Science Self-Correcting?, reports that replication studies, whether successful or unsuccessful, rarely have much effect on citations to the studies being replicated. When a finding fails to replicate, most influential studies sail on, continuing to gather citations at a similar rate for years, as though the replication had never been tried. The issue is not limited to psychology and raises serious questions about how quickly the scientific community corrects itself, and whether replication studies are having the correcting influence that we would like them to have. I considered several possible reasons for the persistent influence on studies that failed to replicate, and concluded that academic search engines like Google Scholar may well be part of the problem, since they prioritize highly cited articles, replicable or not, perpetuating the influence of questionable findings.

The finding that replications don’t affect citations has itself replicated pretty well. A recent blog post by Bob Reed at the University of Canterbury, New Zealand, summarized five recent papers that showed more or less the same thing in psychology, economics, and Nature/Science publications.

In a second article, published just last week in Nature Human Behaviour, Stuart Buck and I suggest ways to Improve academic search engines to reduce scholars’ biases. We suggest that the next generation of academic search engines should do more than count citations, but should help scholars assess studies’ rigor and reliability. We also suggest that future engines should be transparent, responsive and open source.

This seems like a reasonable proposal. The good news is that it’s not necessary for their hypothetical new search engine to dominate or replace existing products. People can use Google Scholar to find the most cited papers and use this new thing to inform about rigor and reliability. A nudge in the right direction, you might say.

A new piranha paper

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Kris Hardies points to this new article, Impossible Hypotheses and Effect-Size Limits, by Wijnand and Lennert van Tilburg, which states:

There are mathematical limits to the magnitudes that population effect sizes can take within the common multivariate context in which psychology is situated, and these limits can be far more restrictive than typically assumed. The implication is that some hypothesized or preregistered effect sizes may be impossible. At the same time, these restrictions offer a way of statistically triangulating the plausible range of unknown effect sizes.

This is closely related to our Piranha Principle, which we first formulated here and then followed up with this paper. It’s great to see more work being done in this area.

Statistical practice as scientific exploration

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This was originally going to happen today, 8 Mar 2024, but it got postponed to some unspecified future date, I don’t know why. In the meantime, here’s the title and abstract:

Statistical practice as scientific exploration

Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University

Much has been written on the philosophy of statistics: How can noisy data, mediated by probabilistic models, inform our understanding of the world? After a brief review of that topic (in short, I am a Bayesian but not an inductivist), I discuss the ways in which researchers when using and developing statistical methods are acting as scientists, forming, evaluating, and elaborating provisional theories about the data and processes they are modeling. This perspective has the conceptual value of pointing toward ways that statistical theory can be expanded to incorporate aspects of workflow that were formally tacit or informal aspects of good practice, and the practical value of motivating tools for improved statistical workflow, as described in part in this article: http://www.stat.columbia.edu/~gelman/research/unpublished/Bayesian_Workflow_article.pdf

The whole thing is kind of mysterious to me. In the email invitation it was called the UPenn Philosophy of Computation and Data Workshop, but then they sent me a flyer where it was called the Philosophy of A.I., Data Science, & Society Workshop in the Quantitative Theory and Methods Department at Emory University. It was going to be on zoom so I guess the particular university affiliation didn’t matter.

In any case, the topic is important, and I’m always interested in speaking with people on the philosophy of statistics. So I hope they get around to rescheduling this one.

With journals, it’s all about the wedding, never about the marriage.

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John “not Jaws” Williams writes:

Here is another example of how hard it is to get erroneous publications corrected, this time from the climatology literature, and how poorly peer review can work.

From the linked article, by Gavin Schmidt:

Back in March 2022, Nicola Scafetta published a short paper in Geophysical Research Letters (GRL) . . . We (me, Gareth Jones and John Kennedy) wrote a note up within a couple of days pointing out how wrongheaded the reasoning was and how the results did not stand up to scrutiny. . . .

After some back and forth on how exactly this would work (including updating the GRL website to accept comments), we reformatted our note as a comment, and submitted it formally on December 12, 2022. We were assured from the editor-in-chief and publications manager that this would be a ‘streamlined’ and ‘timely’ review process. With respect to our comment, that appeared to be the case: It was reviewed, received minor comments, was resubmitted, and accepted on January 28, 2023. But there it sat for 7 months! . . .

The issue was that the GRL editors wanted to have both the comment and a reply appear together. However, the reply had to pass peer review as well, and that seems to have been a bit of a bottleneck. But while the reply wasn’t being accepted, our comment sat in limbo. Indeed, the situation inadvertently gives the criticized author(s) an effective delaying tactic since, as long as a reply is promised but not delivered, the comment doesn’t see the light of day. . . .

All in all, it took 17 months, two separate processes, and dozens of emails, who knows how much internal deliberation, for an official comment to get into the journal pointing issues that were obvious immediately the paper came out. . . .

The odd thing about how long this has taken is that the substance of the comment was produced extremely quickly (a few days) because the errors in the original paper were both commonplace and easily demonstrated. The time, instead, has been entirely taken up by the process itself. . . .

Schmidt also asks a good question:

Why bother? . . . Why do we need to correct the scientific record in formal ways when we have abundant blogs, PubPeer, and social media, to get the message out?

His answer:

Since journals remain extremely reluctant to point to third party commentary on their published papers, going through the journals’ own process seems like it’s the only way to get a comment or criticism noticed by the people who are reading the original article.

Good point. I’m glad that there are people like Schmidt and his collaborators who go to the trouble to correct the public record. I do this from time to time, but mostly I don’t like the stress of dealing with the journals so I’ll just post things here.

My reaction

This story did not surprise me. I’ve heard it a million times, and it’s often happened to me, which is why I once wrote an article called It’s too hard to publish criticisms and obtain data for replication.

Journal editors mostly hate to go back and revise anything. They’re doing volunteer work, and they’re usually in it because they want to publish new and exciting work. Replications, corrections, etc., that’s all seen as boooooring.

With journals, it’s all about the wedding, never about the marriage.

My NYU econ talk will be Thurs 18 Apr 12:30pm (NOT Thurs 7 Mar)

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Hi all. The other day I announced a talk I’ll be giving at the NYU economics seminar. It will be Thurs 18 Apr 12:30pm at 19 West 4th St., room 517.

In my earlier post, I’d given the wrong day for the talk. I’d written that it was this Thurs, 7 Mar. That was wrong! Completely my fault here; I misread my own calendar.

So I hope nobody shows up to that room tomorrow! Thank you for your forbearance.

I hope to see youall on Thurs 18 Apr. Again, here’s the title and abstract:

How large is that treatment effect, really?

“Unbiased estimates” aren’t really unbiased, for a bunch of reasons, including aggregation, selection, extrapolation, and variation over time. Econometrics typically focus on causal identification, with this goal of estimating “the” effect. But we typically care about individual effects (not “Does the treatment work?” but “Where and when does it work?” and “Where and when does it hurt?”). Estimating individual effects is relevant not only for individuals but also for generalizing to the population. For example, how do you generalize from an A/B test performed on a sample right now to possible effects on a different population in the future? Thinking about variation and generalization can change how we design and analyze experiments and observational studies. We demonstrate with examples in social science and public health.

Mindlessness in the interpretation of a study on mindlessness (and why you shouldn’t use the word “whom” in your dating profile)

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This is a long post, so let me give you the tl;dr right away: Don’t use the word “whom” in your dating profile.

OK, now for the story. Fasten your seat belts, it’s going to be a bumpy night.

It all started with this message from Dmitri with subject line, “Man I hate to do this to you but …”, which continued:

How could I resist?

https://www.cnbc.com/2024/02/15/using-this-word-can-make-you-more-influential-harvard-study.html

I’m sorry, let me try again … I had to send this to you BECAUSE this is the kind of obvious shit you like to write about. I like how they didn’t even do their own crappy study they just resurrected one from the distant past.

OK, ok, you don’t need to shout about it!

Following the link we see this breathless press release NBC news story:

Using this 1 word more often can make you 50% more influential, says Harvard study

Sometimes, it takes a single word — like “because” — to change someone’s mind.

That’s according to Jonah Berger, a marketing professor at the Wharton School of the University of Pennsylvania who’s compiled a list of “magic words” that can change the way you communicate. Using the word “because” while trying to convince someone to do something has a compelling result, he tells CNBC Make It: More people will listen to you, and do what you want.

Berger points to a nearly 50-year-old study from Harvard University, wherein researchers sat in a university library and waited for someone to use the copy machine. Then, they walked up and asked to cut in front of the unknowing participant.

They phrased their request in three different ways:

“May I use the Xerox machine?”
“May I use the Xerox machine because I have to make copies?”
“May I use the Xerox machine because I’m in a rush?”
Both requests using “because” made the people already making copies more than 50% more likely to comply, researchers found. Even the second phrasing — which could be reinterpreted as “May I step in front of you to do the same exact thing you’re doing?” — was effective, because it indicated that the stranger asking for a favor was at least being considerate about it, the study suggested.

“Persuasion wasn’t driven by the reason itself,” Berger wrote in a book on the topic, “Magic Words,” which published last year. “It was driven by the power of the word.” . . .

Let’s look into this claim. The first thing I did was click to the study—full credit to CNBC Make It for providing the link—and here’s the data summary from the experiment:

If you look carefully and do some simple calculations, you’ll see that the percentage of participants who complied was 37.5% under treatment 1, 50% under treatment 2, and 62.5% under treatment 3. So, ok, not literally true that both requests using “because” made the people already making copies more than 50% more likely to comply: 0.50/0.375 = 1.33, and increase of 33% is not “more than 50%.” But, sure, it’s a positive result. There were 40 participants in each treatment, so the standard error is approximately 0.5/sqrt(40) = 0.08 for each of those averages. The key difference here is 0.50 – 0.375 = 0.125, that’s the difference between the compliance rates under the treatments “May I use the Xerox machine?” and “May I use the Xerox machine because I have to make copies?”, and this will have a standard error of approximately sqrt(2)*0.08 = 0.11.

The quick summary from this experiment: an observed difference in compliance rates of 12.5 percentage points, with a standard error of 11 percentage points. I don’t want to say “not statistically significant,” so let me just say that the estimate is highly uncertain, so I have no real reason to believe it will replicate.

But wait, you say: the paper was published. Presumably it has a statistically significant p-value somewhere, no? The answer is, yes, they have some “p < .05" results, just not of that particular comparison. Indeed, if you just look at the top rows of that table (Favor = small), then the difference is 0.93 - 0.60 = 0.33 with a standard error of sqrt(0.6*0.4/15 + 0.93*0.07/15) = 0.14, so that particular estimate is just more than two standard errors away from zero. Whew! But now we're getting into forking paths territory: - Noisy data - Small sample - Lots of possible comparisons - Any comparison that's statistically significant will necessarily be huge - Open-ended theoretical structure that could explain just about any result. I'm not saying the researchers were trying to anything wrong. But remember, honesty and transparency are not enuf. Such a study is just too noisy to be useful.

But, sure, back in the 1970s many psychology researchers not named Meehl weren’t aware of these issues. They seem to have been under the impression that if you gather some data and find something statistically significant for which you could come up with a good story, that you’d discovered a general truth.

What’s less excusable is a journalist writing this in the year 2024. But it’s no surprise, conditional on the headline, “Using this 1 word more often can make you 50% more influential, says Harvard study.”

But what about that book by the University of Pennsylvania marketing professor? I searched online, and, fortunately for us, the bit about the Xerox machine is right there in the first chapter, in the excerpt we can read for free. Here it is:

He got it wrong, just like the journalist did! It’s not true that including the meaningless reason increased persuasion just as much as the valid reason did. Look at the data! The outcomes under the three treatment were 37.5%, 50%, and 62.5%. 50% – 37.5% ≠ 62.5% – 37.5%. Ummm, ok, he could’ve said something like, “Among a selected subset of the data with only 15 or 16 people in each treatment, including the meaningless reason increased persuasion just as much as the valid reason did.” But that doesn’t sound so impressive! Even if you add something like, “and it’s possible to come up with a plausible theory to go with this result.”

The book continues:

Given the flaws in the description of the copier study, I’m skeptical about these other claims.

But let me say this. If it is indeed true that using the word “whom” in online dating profiles makes you 31% more likely to get a date, then my advice is . . . don’t use the word “whom”! Think of it from a potential-outcomes perspective. Sure, you want to get a date. But do you really want to go on a date with someone who will only go out with you if you use the word “whom”?? That sounds like a really pretentious person, not a fun date at all!

OK, I haven’t read the rest of the book, and it’s possible that somewhere later on the author says something like, “OK, I was exaggerating a bit on page 4 . . .” I doubt it, but I guess it’s possible.

Replications, anyone?

To return to the topic at hand: In 1978 a study was conducted with 120 participants in a single location. The study was memorable enough to be featured in a business book nearly fifty years later.

Surely the finding has been replicated?

I’d imagine yes; on the other hand, if it had been replicated, this would’ve been mentioned in the book, right? So it’s hard to know.

I did a search, and the article does seem to have been influential:

It’s been cited 1514 times—that’s a lot! Google lists 55 citations in 2023 alone, and in what seem to be legit journals: Human Communication Research, Proceedings of the ACM, Journal of Retailing, Journal of Organizational Behavior, Journal of Applied Psychology, Human Resources Management Review, etc. Not core science journals, exactly, but actual applied fields, with unskeptical mentions such as:

What about replications? I searched on *langer blank chanowitz 1978 replication* and found this paper by Folkes (1985), which reports:

Four studies examined whether verbal behavior is mindful (cognitive) or mindless (automatic). All studies used the experimental paradigm developed by E. J. Langer et al. In Studies 1–3, experimenters approached Ss at copying machines and asked to use it first. Their requests varied in the amount and kind of information given. Study 1 (82 Ss) found less compliance when experimenters gave a controllable reason (“… because I don’t want to wait”) than an uncontrollable reason (“… because I feel really sick”). In Studies 2 and 3 (42 and 96 Ss, respectively) requests for controllable reasons elicited less compliance than requests used in the Langer et al study. Neither study replicated the results of Langer et al. Furthermore, the controllable condition’s lower compliance supports a cognitive approach to social interaction. In Study 4, 69 undergraduates were given instructions intended to increase cognitive processing of the requests, and the pattern of compliance indicated in-depth processing of the request. Results provide evidence for cognitive processing rather than mindlessness in social interaction.

So this study concludes that the result didn’t replicate at all! On the other hand, it’s only a “partial replication,” and indeed they do not use the same conditions and wording as in the original 1978 paper. I don’t know why not, except maybe that exact replications traditionally get no respect.

Langer et al. responded in that journal, writing:

We see nothing in her results [Folkes (1985)] that would lead us to change our position: People are sometimes mindful and sometimes not.

Here they’re referring to the table from the 1978 study, reproduced at the top of this post, which shows a large effect of the “because I have to make copies” treatment under the “Small Favor” condition but no effect under the “Large Favor” condition. Again, given the huge standard errors here, we can’t take any of this seriously, but if you just look at the percentages without considering the uncertainty, then, sure, that’s what they found. Thus, in their response to the partial replication study that did not reproduce their results, Langer et al. emphasized that their original finding was not a main effect but an interaction: “People are sometimes mindful and sometimes not.”

That’s fine. Psychology studies often measure interactions, as they should: the world is a highly variable place.

But, in that case, everyone’s been misinterpreting that 1978 paper! When I say “everybody,” I mean this recent book by the business school professor and also the continuing references to the paper in the recent literature.

Here’s the deal. The message that everyone seems to have learned, or believed they learned, from the 1978 paper is that meaningless explanations are as good as meaningful explanations. But, according to the authors of that paper when they responded to criticism in 1985, the true message is that this trick works sometimes and sometimes not. That’s a much weaker message.

Indeed the study at hand is too small to draw any reliable conclusions about any possible interaction here. The most direct estimate of the interaction effect from the above table is (0.93 – 0.60) – (0.24 – 0.24) = 0.33, with a standard error of sqrt(0.93*0.07/15 + 0.60*0.40/15 + 0.24*0.76/25 + 0.24*0.76/25) = 0.19. So, no, I don’t see much support for the claim in this post from Psychology Today:

So what does this all mean? When the stakes are low people will engage in automatic behavior. If your request is small, follow your request with the word “because” and give a reason—any reason. If the stakes are high, then there could be more resistance, but still not too much.

This happens a lot in unreplicable or unreplicated studies: a result is found under some narrow conditions, and then it is taken to have very general implications. This is just an unusual case where the authors themselves pointed out the issue. As they wrote in their 1985 article:

The larger concern is to understand how mindlessness works, determine its consequences, and specify better the conditions under which it is and is not likely to occur.

That’s a long way from the claim in that business book that “because” is a “magic word.”

Like a lot of magic, it only works under some conditions, and you can’t necessarily specify those conditions ahead of time. It works when it works.

There might be other replication studies of this copy machine study. I guess you couldn’t really do it now, because people don’t spend much time waiting at the copier. But the office copier was a thing for several decades. So maybe there are even some exact replications out there.

In searching for a replication, I did come across this post from 2009 by Mark Liberman that criticized yet another hyping of that 1978 study, this time from a paper by psychologist Daniel Kahenman in the American Economic Review. Kahneman wrote:

Ellen J. Langer et al. (1978) provided a well-known example of what she called “mindless behavior.” In her experiment, a confederate tried to cut in line at a copying machine, using various preset “excuses.” The conclusion was that statements that had the form of an unqualified request were rejected (e.g., “Excuse me, may I use the Xerox machine?”), but almost any statement that had the general form of an explanation was accepted, including “Excuse me, may I use the Xerox machine because I want to make copies?” The superficiality is striking.

As Liberman writes, this represented a “misunderstanding of the 1978 paper’s results, involving both a different conclusion and a strikingly overgeneralized picture of the observed effects.” Liberman performs an analysis of the data from that study which is similar to what I have done above.

Liberman summarizes:

The problem with Prof. Kahneman’s interpretation is not that he took the experiment at face value, ignoring possible flaws of design or interpretation. The problem is that he took a difference in the distribution of behaviors between one group of people and another, and turned it into generic statements about the behavior of people in specified circumstances, as if the behavior were uniform and invariant. The resulting generic statements make strikingly incorrect predictions even about the results of the experiment in question, much less about life in general.

Mindfulness

The key claim of all this research is that people are often mindless: they respond to the form of a request without paying attention to its context, with “because” acting as a “magic word.”

I would argue that this is exactly the sort of mindless behavior being exhibited by the people who are promoting that copying-machine experiment! They are taking various surface aspects of the study and using it to draw large, unsupported conclusions, without being mindful of the details.

In this case, the “magic words” are things like “p < .05," "randomized experiment," "Harvard," "peer review," and "Journal of Personality and Social Psychology" (this notwithstanding). The mindlessness comes from not looking into what exactly was in the paper being cited.

In conclusion . . .

So, yeah, thanks for nothing, Dmitri! Three hours of my life spent going down a rabbit hole. But, hey, if any readers who are single have read far enough down in the post to see my advice not to use “whom” in your data profile, it will all have been worth it.

Seriously, though, the “mindlessness” aspect of this story is interesting. The point here is not, Hey, a 50-year-old paper has some flaws! Or the no-less-surprising observation: Hey, a pop business book exaggerates! The part that fascinates me is that there’s all this shaky research that’s being taken as strong evidence that consumers are mindless—and the people hyping these claims are themselves demonstrating the point by mindlessly following signals without looking into the evidence.

The ultimate advice that the mindfulness gurus are giving is not necessarily so bad. For example, here’s the conclusion of that online article about the business book:

Listen to the specific words other people use, and craft a response that speaks their language. Doing so can help drive an agreement, solution or connection.

“Everything in language we might use over email at the office … [can] provide insight into who they are and what they’re going to do in the future,” says Berger.

That sounds ok. Just forget all the blather about the “magic words” and the “superpowers,” and forget the unsupported and implausible claim that “Arguments, requests and presentations aren’t any more or less convincing when they’re based on solid ideas.” As often is the case, I think these Ted-talk style recommendations would be on more solid ground if they were just presented as the product of common sense and accumulated wisdom, rather than leaning on some 50-year-old psychology study that just can’t bear the weight. But maybe you can’t get the airport book and the Ted talk without a claim of scientific backing.

Don’t get me wrong here. I’m not attributing any malign motivations to any of the people involved in this story (except for Dmitri, I guess). I’m guessing they really believe all this. And I’m not using “mindless” as an insult. We’re all mindless sometimes—that’s the point of the Langer et al. (1978) study; it’s what Herbert Simon called “bounded rationality.” The trick is to recognize your areas of mindlessness. If you come to an area where you’re being mindless, don’t write a book about it! Even if you naively think you’ve discovered a new continent. As Mark Twain apparently never said, it ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.

The usual disclaimer

I’m not saying the claims made by Langer et al. (1978) are wrong. Maybe it’s true that, under conditions of mindlessness, all that matters is the “because” and any empty explanation will do; maybe the same results would show up in a preregistered replication. All I’m saying is that the noisy data that have been presented don’t provide any strong evidence in support of such claims, and that’s what bothers me about all those confident citations in the business literature.

P.S.

After writing the above post, I sent this response to Dmitri:

OK, I just spent 3 hours on this. I now have to figure out what to do with this after blogging it, because I think there are some important points here. Still, yeah, you did a bad thing by sending this to me. These are 3 hours I could’ve spent doing real work, or relaxing . . .

He replied:

I mean, yeah, that’s too bad for you, obviously. But … try to think about it from my point of view. I am more influential, I got you to work on this while I had a nice relaxing post-Valentine’s day sushi meal with my wife (much easier to get reservations on the 15th and the flowers are a lot cheaper), while you were toiling away on what is essentially my project. I’d say the magic words did their job.

Good point! He exploited my mindlessness. I responded:

Ok, I’ll quote you on that one too! (minus the V-day details).

I’m still chewing on your comment that you appreciate the Beatles for their innovation as much as for their songs. The idea that there are lots of songs of similar quality but not so much innovation, that’s interesting. The only thing is that I don’t know enough about music, even pop music, to have a mental map of where everything fits in. For example, I recently heard that Coldplay song, and it struck me that it was in the style of U2 . But I don’t really know if U2 was the originator of that soaring sound. I guess Pink Floyd is kinda soaring too, but not quite in the same way . . . etc etc … the whole thing was frustrating to me because I had no sense of whether I was entirely bullshitting or not.

So if you can spend 3 hours writing a post on the above topic, we’ll be even.

Dmitri replied:

I am proud of the whole “Valentine’s day on the 15th” trick, so you are welcome to include it. That’s one of our great innovations. After the first 15-20 Valentine’s days, you can just move the date a day later and it is much easier.

And, regarding the music, he wrote:

U2 definitely invented a sound, with the help of their producer Brian Eno.

It is a pretty safe bet that every truly successful musician is an innovator—once you know the sound it is easy enough to emulate. Beethoven, Charlie Parker, the Beatles, all the really important guys invented a forceful, effective new way of thinking about music.

U2 is great, but when I listened to an entire U2 song from beginning to end, it seemed so repetitive as to be unlistenable. I don’t feel that way about the Beatles or REM. But just about any music sounds better to me in the background, which I think is a sign of my musical ignorance and tone-deafness (for real, I’m bad at recognizing pitches) more than anything else. I guess the point is that you’re supposed to dance to it, not just sit there and listen.

Anyway, I warned Dmitri about what would happen if I post his Valentine’s Day trick:

I post this, then it will catch on, and it will no longer work . . . just warning ya! You’ll have to start doing Valentine’s Day on the 16th, then the 17th, . . .

To which Dmitri responded:

Yeah but if we stick with it, it will roll around and we will get back to February 14 while everyone else is celebrating Valentines Day on these weird wrong days!

I’ll leave him with the last word.

How large is that treatment effect, really? (My talk at the NYU economics seminar, Thurs 7 Mar 18 Apr)

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Thurs 7 Mar 18 Apr 2024, 12:30pm at 19 West 4th St., room 517:

How large is that treatment effect, really?

“Unbiased estimates” aren’t really unbiased, for a bunch of reasons, including aggregation, selection, extrapolation, and variation over time. Econometrics typically focus on causal identification, with this goal of estimating “the” effect. But we typically care about individual effects (not “Does the treatment work?” but “Where and when does it work?” and “Where and when does it hurt?”). Estimating individual effects is relevant not only for individuals but also for generalizing to the population. For example, how do you generalize from an A/B test performed on a sample right now to possible effects on a different population in the future? Thinking about variation and generalization can change how we design and analyze experiments and observational studies. We demonstrate with examples in social science and public health.

Our new book, Active Statistics, is now available!

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Coauthored with Aki Vehtari, this new book is lots of fun, perhaps the funnest I’ve ever been involved in writing. And it’s stuffed full of statistical insights. The webpage for the book is here, and the link to buy it is here or directly from the publisher here.

With hundreds of stories, activities, and discussion problems on applied statistics and causal inference, this book is a perfect teaching aid, a perfect adjunct to a self-study program, and an enjoyable bedside read if you already know some statistics.

Here’s the quick summary:

This book provides statistics instructors and students with complete classroom material for a one- or two-semester course on applied regression and causal inference. It is built around 52 stories, 52 class-participation activities, 52 hands-on computer demonstrations, and 52 discussion problems that allow instructors and students to explore the real-world complexity of the subject. The book fosters an engaging “flipped classroom” environment with a focus on visualization and understanding. The book provides instructors with frameworks for self-study or for structuring the course, along with tips for maintaining student engagement at all levels, and practice exam questions to help guide learning. Designed to accompany the authors’ previous textbook Regression and Other Stories, its modular nature and wealth of material allow this book to be adapted to different courses and texts or be used by learners as a hands-on workbook.

It’s got 52 of everything because it’s structured around a two-semester class, with 13 weeks per semester and 2 classes per week. It’s really just bursting with material, including some classic stories and lots of completely new material. Right off the bat we present a statistical mystery that arose with a Wikipedia experiment, and we have a retelling of the famous Literary Digest survey story but with a new and unexpected twist (courtesy of Sharon Lohr and J. Michael Brick). And the activities have so much going on! One of my favorites is a version of the two truths and a lie game that demonstrates several different statistical ideas.

People have asked how this differs from my book with Deborah Nolan, Teaching Statistics: A Bag of Tricks. My quick answer is that Active Statistics has a lot more of everything, it’s structured to cover an entire two-semester course in order, and it’s focused on applied statistics. Including a bunch of stories, activities, demonstrations, and problems on causal inference, a topic that is not always so well integrated into the statistics curriculum. You’re gonna love this book.

You can buy it here or here. It’s only 25 bucks, which is an excellent deal considering how stuffed it is with useful content. Enjoy.