andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2036 knowledge-graph by maker-knowledge-mining
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Introduction: Interesting discussion here from Mark Palko. I think of Palko’s post as having a lot of statistical content here, although it’s hard for me to say exactly why it feels that way to me. Perhaps it has to do with the challenges of measurement, how something that would seem to be a simple problem of measurement (adding up the cost of staple foods) isn’t so easy after all, in fact it requires a lot of subject-matter knowledge, in this case knowledge that some guy named Ron Shaich whom I’ve never heard of (but that’s ok, I’m sure he’s never heard of me either) doesn’t have. We’ve been talking a lot about measurement on this blog recently (for example, here ), and I think this new story fits into these discussions somehow.
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2 We’ve been talking a lot about measurement on this blog recently (for example, here ), and I think this new story fits into these discussions somehow. [sent-4, score-1.352]
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same-blog 1 0.99999988 2036 andrew gelman stats-2013-09-24-“Instead of the intended message that being poor is hard, the takeaway is that rich people aren’t very good with money.”
Introduction: Interesting discussion here from Mark Palko. I think of Palko’s post as having a lot of statistical content here, although it’s hard for me to say exactly why it feels that way to me. Perhaps it has to do with the challenges of measurement, how something that would seem to be a simple problem of measurement (adding up the cost of staple foods) isn’t so easy after all, in fact it requires a lot of subject-matter knowledge, in this case knowledge that some guy named Ron Shaich whom I’ve never heard of (but that’s ok, I’m sure he’s never heard of me either) doesn’t have. We’ve been talking a lot about measurement on this blog recently (for example, here ), and I think this new story fits into these discussions somehow.
2 0.14548478 1318 andrew gelman stats-2012-05-13-Stolen jokes
Introduction: Fun stories here (from Kliph Nesteroff, link from Mark Palko).
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Introduction: I’ve recently decided that statistics lies at the intersection of measurement, variation, and comparison. (I need to use some cool Venn-diagram-drawing software to show this.) I’ll argue this one another time–my claim is that, to be “statistics,” you need all three of these elements, no two will suffice-. My point here, though, is that as statisticians, we teach all of these three things and talk about how important they are (and often criticize/mock others for selection bias and other problems that arise from not recognizing the difficulties of good measurement, attention to variation, and focused comparisons), but in our own lives (in deciding how to teach and do research, administration, and service–not to mention our personal lives), we think about these issues almost not at all . In our classes, we almost never use standardized tests, let alone the sort of before-after measurements we recommend to others. We do not evaluate our plans systematically nor do we typically e
Introduction: Just in time for Halloween, here’s a horror story for you . . . Howard Wainer writes: In my book “Uneducated Guesses” in the chapter on value-added models, I discuss how the treatment of missing data can have a profound effect on the estimates of teacher scores. I made up how a principal might send the best students on a field trip at the beginning of the year when the ‘pre-test’ was given (and their scores would be imputed from the students who showed up) and that the bottom half of the class would have a matching field trip on the day of the post test. Everyone laughed. But apparently someone decided to take it seriously. http://www.amren.com/news/2012/10/el-paso-schools-confront-scandal-of-students-who-disappeared-at-test-time/ http://www.elpasotimes.com/episd/ci_20848628/former-episd-superintendent-lorenzo-garcia-enter-plea-aggreement You can’t make this stuff up. This sort of thing is not surprising but it’s worth keeping in mind. That a measurement system c
5 0.10732844 279 andrew gelman stats-2010-09-15-Electability and perception of electability
Introduction: Mark Palko writes: We’ve heard a lot recently about the Republican voters going with less electable candidates (last night in particular), but I [Palko] wonder whether this is less a question of putting less weight on electability and more of having a different perception of electability. Is this really a case of primary voters who supported O’Donnell saying “I’d rather be right than be president” or do a large percentage of them believe she has a better than even chance in November. My reply: It’s not so horrible for people to engage in non-strategic voting! Beyond the immediate probabilities of this candidate winning the Senate election in November, primary challenges keep incumbents accountable. The thing I don’t really understand is why there aren’t more such challenges. I suppose they’re unlikely enough to succeed that it’s not usually worth doing it and risking your political career. But, yes, I’m pretty sure that O’Donnell’s voters overestimated the chance that she’
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same-blog 1 0.97369117 2036 andrew gelman stats-2013-09-24-“Instead of the intended message that being poor is hard, the takeaway is that rich people aren’t very good with money.”
Introduction: Interesting discussion here from Mark Palko. I think of Palko’s post as having a lot of statistical content here, although it’s hard for me to say exactly why it feels that way to me. Perhaps it has to do with the challenges of measurement, how something that would seem to be a simple problem of measurement (adding up the cost of staple foods) isn’t so easy after all, in fact it requires a lot of subject-matter knowledge, in this case knowledge that some guy named Ron Shaich whom I’ve never heard of (but that’s ok, I’m sure he’s never heard of me either) doesn’t have. We’ve been talking a lot about measurement on this blog recently (for example, here ), and I think this new story fits into these discussions somehow.
2 0.74147469 1084 andrew gelman stats-2011-12-26-Tweeting the Hits?
Introduction: Someone sent me an email saying that he liked my little essay, “Descriptive statistics aren’t just for losers.” I had no idea what he was talking about, but it sounded like the kind of thing I’d say, so I searched the blog and found this post , which indeed I really like! I thanked my correspondent for reminding me of this little article I’d forgotten, and he told me he just learned of it via someone’s tweet. This made me think: Maybe I should have a twitter feed of nothing but old blog entries. I could just go back to 2004 and then go gradually forward, tweeting the items that I judge to remain of interest. Does this make sense? Or is there a better way to do this? ALternatively, I could do it as a separate blog, but that seems a bit . . . recursive.
3 0.7398957 1278 andrew gelman stats-2012-04-23-“Any old map will do” meets “God is in every leaf of every tree”
Introduction: As a statistician I am particularly worried about the rhetorical power of anecdotes (even though I use them in my own reasoning; see discussion below). But much can be learned from a true anecdote. The rough edges—the places where the anecdote doesn’t fit your thesis—these are where you learn. We have recently had a discussion ( here and here ) of Karl Weick, a prominent scholar of business management who plagiarized a story and then went on to draw different lessons from the pilfered anecdote in several different publications published over many years. Setting aside an issues of plagiarism and rulebreaking, I argue that, by hiding the source of the story and changing its form, Weick and his management-science audience are losing their ability to get anything out of it beyond empty confirmation. A full discussion follows. 1. The lost Hungarian soldiers Thomas Basbøll (who has the unusual (to me) job of “writing consultant” at the Copenhagen Business School) has been
4 0.73164499 582 andrew gelman stats-2011-02-20-Statisticians vs. everybody else
Introduction: Statisticians are literalists. When someone says that the U.K. boundary commission’s delay in redistricting gave the Tories an advantage equivalent to 10 percent of the vote, we’re the kind of person who looks it up and claims that the effect is less than 0.7 percent. When someone says, “Since 1968, with the single exception of the election of George W. Bush in 2000, Americans have chosen Republican presidents in times of perceived danger and Democrats in times of relative calm,” we’re like, Hey, really? And we go look that one up too. And when someone says that engineers have more sons and nurses have more daughters . . . well, let’s not go there. So, when I was pointed to this blog by Michael O’Hare making the following claim, in the context of K-12 education in the United States: My [O'Hare's] favorite examples of this junk [educational content with no workplace value] are spelling and pencil-and-paper algorithm arithmetic. These are absolutely critical for a clerk
5 0.72979605 2365 andrew gelman stats-2014-06-09-I hate polynomials
Introduction: A recent discussion with Mark Palko [scroll down to the comments at this link ] reminds me that I think that polynomials are way way overrated, and I think a lot of damage has arisen from the old-time approach of introducing polynomial functions as a canonical example of linear regressions ( for example ). There are very few settings I can think of where it makes sense to fit a general polynomial of degree higher than 2. I think that millions of students have been brainwashed into thinking of these as the canonical functions and that this has caused endless trouble later on. I’m not sure how I’d change the high school math curriculum to deal with this, but I do think it’s an issue.
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same-blog 1 0.96805501 2036 andrew gelman stats-2013-09-24-“Instead of the intended message that being poor is hard, the takeaway is that rich people aren’t very good with money.”
Introduction: Interesting discussion here from Mark Palko. I think of Palko’s post as having a lot of statistical content here, although it’s hard for me to say exactly why it feels that way to me. Perhaps it has to do with the challenges of measurement, how something that would seem to be a simple problem of measurement (adding up the cost of staple foods) isn’t so easy after all, in fact it requires a lot of subject-matter knowledge, in this case knowledge that some guy named Ron Shaich whom I’ve never heard of (but that’s ok, I’m sure he’s never heard of me either) doesn’t have. We’ve been talking a lot about measurement on this blog recently (for example, here ), and I think this new story fits into these discussions somehow.
2 0.95549995 2093 andrew gelman stats-2013-11-07-I’m negative on the expression “false positives”
Introduction: After seeing a document sent to me and others regarding the crisis of spurious, statistically-significant research findings in psychology research, I had the following reaction: I am unhappy with the use in the document of the phrase “false positives.” I feel that this expression is unhelpful as it frames science in terms of “true” and “false” claims, which I don’t think is particularly accurate. In particular, in most of the recent disputed Psych Science type studies (the ESP study excepted, perhaps), there is little doubt that there is _some_ underlying effect. The issue, as I see it, as that the underlying effects are much smaller, and much more variable, than mainstream researchers imagine. So what happens is that Psych Science or Nature or whatever will publish a result that is purported to be some sort of universal truth, but it is actually a pattern specific to one data set, one population, and one experimental condition. In a sense, yes, these journals are publishing
Introduction: Chris Masse points me to this response by Daryl Bem and two statisticians (Jessica Utts and Wesley Johnson) to criticisms by Wagenmakers et.al. of Bem’s recent ESP study. I have nothing to add but would like to repeat a couple bits of my discussions of last month, of here : Classical statistical methods that work reasonably well when studying moderate or large effects (see the work of Fisher, Snedecor, Cochran, etc.) fall apart in the presence of small effects. I think it’s naive when people implicitly assume that the study’s claims are correct, or the study’s statistical methods are weak. Generally, the smaller the effects you’re studying, the better the statistics you need. ESP is a field of small effects and so ESP researchers use high-quality statistics. To put it another way: whatever methodological errors happen to be in the paper in question, probably occur in lots of researcher papers in “legitimate” psychology research. The difference is that when you’re studying a
Introduction: Type S error: When your estimate is the wrong sign, compared to the true value of the parameter Type M error: When the magnitude of your estimate is far off, compared to the true value of the parameter More here.
5 0.94910407 1363 andrew gelman stats-2012-06-03-Question about predictive checks
Introduction: Klaas Metselaar writes: I [Metselaar] am currently involved in a discussion about the use of the notion “predictive” as used in “posterior predictive check”. I would argue that the notion “predictive” should be reserved for posterior checks using information not used in the determination of the posterior. I quote from the discussion: “However, the predictive uncertainty in a Bayesian calculation requires sampling from all the random variables, and this includes both the model parameters and the residual error”. My [Metselaar's] comment: This may be exactly the point I am worried about: shouldn’t the predictive uncertainty be defined as sampling from the posterior parameter distribution + residual error + sampling from the prediction error distribution? Residual error reduces to measurement error in the case of a model which is perfect for the sample of experiments. Measurement error could be reduced to almost zero by ideal and perfect measurement instruments. I would h
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