andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-638 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: This is a chance for me to combine two of my interests–politics and statistics–and probably to irritate both halves of the readership of this blog. Anyway… I recently wrote about the apparent correlation between Bayes/non-Bayes statistical ideology and liberal/conservative political ideology: The Bayes/non-Bayes fissure had a bit of a political dimension–with anti-Bayesians being the old-line conservatives (for example, Ronald Fisher) and Bayesians having a more of a left-wing flavor (for example, Dennis Lindley). Lots of counterexamples at an individual level, but my impression is that on average the old curmudgeonly, get-off-my-lawn types were (with some notable exceptions) more likely to be anti-Bayesian. This was somewhat based on my experiences at Berkeley. Actually, some of the cranky anti-Bayesians were probably Democrats as well, but when they were being anti-Bayesian they seemed pretty conservative. Recently I received an interesting item from Gerald Cliff, a pro
sentIndex sentText sentNum sentScore
1 This is a chance for me to combine two of my interests–politics and statistics–and probably to irritate both halves of the readership of this blog. [sent-1, score-0.157]
2 Lots of counterexamples at an individual level, but my impression is that on average the old curmudgeonly, get-off-my-lawn types were (with some notable exceptions) more likely to be anti-Bayesian. [sent-3, score-0.151]
3 Actually, some of the cranky anti-Bayesians were probably Democrats as well, but when they were being anti-Bayesian they seemed pretty conservative. [sent-5, score-0.092]
4 I admit that my attitudes towards Bayesian statistics come from him. [sent-9, score-0.115]
5 He said that if one has a population with a normal distribution and unknown mean which one is trying to estimate, it is foolish to assume that the mean is random; it is fixed, and currently unknown to the statistician, but one should not assume that it is a random variable. [sent-10, score-0.796]
6 I never met Neyman while I was at Berkeley (he had passed away before I got there) but I’ve heard that he was very liberal politically (as was David Blackwell). [sent-14, score-0.152]
7 Regarding the normal distribution comment below, I would say: 1. [sent-15, score-0.285]
8 Bayesians consider parameters to be fixed but unknown. [sent-16, score-0.086]
9 The prior distribution is a regularization tool that allows more stable estimates. [sent-17, score-0.158]
10 The biggest assumptions in probability models are typically not the prior distribution but in the data model. [sent-19, score-0.228]
11 In this case, Wolfowitz was willing to assume a normal distribution with no question but then balked at using any knowledge about its mean. [sent-20, score-0.498]
12 It seems odd to me, as a Bayesian, for one’s knowledge to be divided so sharply: zero knowledge about the parameter, perfect certainty about the distributional family. [sent-21, score-0.302]
13 To return to the political dimension: From basic principles, I don’t see any strong logical connection between Bayesianism and left-wing politics. [sent-22, score-0.257]
14 Statisticians are typically worried about messing with data, which perhaps is one reason that the Current Index to Statistics lists 131 articles with “conservative” in the title or keywords and only 46 with the words “liberal” or “radical. [sent-25, score-0.242]
15 ” In that sense, given that, until recently, non-Bayesian approaches were the norm in statistics, it was the more radical group of statisticians (on average) who wanted to try something different. [sent-26, score-0.192]
16 As noted above, I don’t think these connections make much logical sense but I can see where they were coming from (with exceptions, of course, as noted regarding Neyman above). [sent-28, score-0.342]
wordName wordTfidf (topN-words)
[('wolfowitz', 0.366), ('conservative', 0.223), ('cliff', 0.159), ('distribution', 0.158), ('liberal', 0.152), ('political', 0.148), ('conservatism', 0.147), ('lindley', 0.129), ('neyman', 0.129), ('normal', 0.127), ('exceptions', 0.125), ('unknown', 0.123), ('knowledge', 0.115), ('statistics', 0.115), ('dimension', 0.115), ('statisticians', 0.112), ('logical', 0.109), ('ideology', 0.108), ('bayesians', 0.103), ('assume', 0.098), ('bayesian', 0.095), ('curmudgeonly', 0.092), ('fissure', 0.092), ('cranky', 0.092), ('messing', 0.092), ('recently', 0.087), ('blackwell', 0.086), ('ally', 0.086), ('irritate', 0.086), ('vietnam', 0.086), ('fixed', 0.086), ('continuity', 0.083), ('gerald', 0.083), ('socialism', 0.083), ('counterexamples', 0.083), ('leftist', 0.083), ('sharply', 0.083), ('noted', 0.081), ('keywords', 0.08), ('crucially', 0.08), ('radical', 0.08), ('jakulin', 0.075), ('illinois', 0.074), ('distributional', 0.072), ('nominal', 0.071), ('readership', 0.071), ('regarding', 0.071), ('typically', 0.07), ('random', 0.069), ('notable', 0.068)]
simIndex simValue blogId blogTitle
same-blog 1 0.99999982 638 andrew gelman stats-2011-03-30-More on the correlation between statistical and political ideology
Introduction: This is a chance for me to combine two of my interests–politics and statistics–and probably to irritate both halves of the readership of this blog. Anyway… I recently wrote about the apparent correlation between Bayes/non-Bayes statistical ideology and liberal/conservative political ideology: The Bayes/non-Bayes fissure had a bit of a political dimension–with anti-Bayesians being the old-line conservatives (for example, Ronald Fisher) and Bayesians having a more of a left-wing flavor (for example, Dennis Lindley). Lots of counterexamples at an individual level, but my impression is that on average the old curmudgeonly, get-off-my-lawn types were (with some notable exceptions) more likely to be anti-Bayesian. This was somewhat based on my experiences at Berkeley. Actually, some of the cranky anti-Bayesians were probably Democrats as well, but when they were being anti-Bayesian they seemed pretty conservative. Recently I received an interesting item from Gerald Cliff, a pro
2 0.27083197 604 andrew gelman stats-2011-03-08-More on the missing conservative psychology researchers
Introduction: Will Wilkinson adds to the discussion of Jonathan Haidt’s remarks regarding the overwhelming prevalance of liberal or left-wing attitudes among psychology professors. I pretty much agree with Wilkinson’s overview: Folks who constantly agree with one another grow insular, self-congratulatory, and not a little lazy. The very possibility of disagreement starts to seem weird or crazy. When you’re trying to do science about human beings, this attitude’s not so great. Wilkinson also reviewed the work of John Jost in this area. Jost is a psychology researcher with the expected liberal/left political leanings, but his relevance here is that he has actually done research on political attitudes and personality types. In Wilkinson’s words: Jost has done plenty of great work that helps explain not only why the best minds in science are liberal, but why most scientists-most academics, even-are liberal. Individuals with the personality trait that most strongly predicts an inclinati
3 0.15605126 1140 andrew gelman stats-2012-01-27-Educational monoculture
Introduction: John Cook writes that he’d like to hear more people talk about “educational monoculture.” I don’t actually know John Cook but I enjoy reading his blog, so I feel like the least I can do is to honor his request. I have to admit that I have a bit of a monocultural temperament myself. I have strong feelings about the right and wrong way to do things, and I don’t have much patience for what seems to me to be the wrong way. As a result, I’ve often disparaged or ignored important statistical developments because some small aspect of the new idea didn’t fit with my thinking. (On the plus side, I think I’ve disparaged or ignored lots more bad ideas thad deserve oblivion.) I’ve always been suspicious of the hedgehog/fox distinction because my impression is that just about everybody likes to think of him or herself as a fox. Being a hedgehog is like being “ideological”; most of us like to think of ourselves as pragmatic foxes. And in any case I think most statisticians are foxes.
4 0.15312512 1695 andrew gelman stats-2013-01-28-Economists argue about Bayes
Introduction: Robert Bell pointed me to this post by Brad De Long on Bayesian statistics, and then I also noticed this from Noah Smith, who wrote: My impression is that although the Bayesian/Frequentist debate is interesting and intellectually fun, there’s really not much “there” there… despite being so-hip-right-now, Bayesian is not the Statistical Jesus. I’m happy to see the discussion going in this direction. Twenty-five years ago or so, when I got into this biz, there were some serious anti-Bayesian attitudes floating around in mainstream statistics. Discussions in the journals sometimes devolved into debates of the form, “Bayesians: knaves or fools?”. You’d get all sorts of free-floating skepticism about any prior distribution at all, even while people were accepting without question (and doing theory on) logistic regressions, proportional hazards models, and all sorts of strong strong models. (In the subfield of survey sampling, various prominent researchers would refuse to mode
5 0.14562613 639 andrew gelman stats-2011-03-31-Bayes: radical, liberal, or conservative?
Introduction: Radford writes : The word “conservative” gets used many ways, for various political purposes, but I would take it’s basic meaning to be someone who thinks there’s a lot of wisdom in traditional ways of doing things, even if we don’t understand exactly why those ways are good, so we should be reluctant to change unless we have a strong argument that some other way is better. This sounds very Bayesian, with a prior reducing the impact of new data. I agree completely, and I think Radford will very much enjoy my article with Aleks Jakulin , “Bayes: radical, liberal, or conservative?” Radford’s comment also fits with my increasing inclination to use informative prior distributions.
6 0.13564689 2072 andrew gelman stats-2013-10-21-The future (and past) of statistical sciences
7 0.13216124 1204 andrew gelman stats-2012-03-08-The politics of economic and statistical models
8 0.13165291 1779 andrew gelman stats-2013-03-27-“Two Dogmas of Strong Objective Bayesianism”
9 0.12937909 1469 andrew gelman stats-2012-08-25-Ways of knowing
10 0.12816367 2255 andrew gelman stats-2014-03-19-How Americans vote
11 0.12317369 1955 andrew gelman stats-2013-07-25-Bayes-respecting experimental design and other things
12 0.12278064 1941 andrew gelman stats-2013-07-16-Priors
13 0.12218271 1869 andrew gelman stats-2013-05-24-In which I side with Neyman over Fisher
14 0.1218369 1560 andrew gelman stats-2012-11-03-Statistical methods that work in some settings but not others
15 0.12046705 1385 andrew gelman stats-2012-06-20-Reconciling different claims about working-class voters
16 0.11679703 1474 andrew gelman stats-2012-08-29-More on scaled-inverse Wishart and prior independence
17 0.11401752 781 andrew gelman stats-2011-06-28-The holes in my philosophy of Bayesian data analysis
18 0.1139748 1247 andrew gelman stats-2012-04-05-More philosophy of Bayes
19 0.11248532 961 andrew gelman stats-2011-10-16-The “Washington read” and the algebra of conditional distributions
20 0.11169925 1527 andrew gelman stats-2012-10-10-Another reason why you can get good inferences from a bad model
topicId topicWeight
[(0, 0.224), (1, 0.076), (2, 0.042), (3, 0.072), (4, -0.114), (5, 0.025), (6, 0.001), (7, 0.067), (8, -0.062), (9, -0.0), (10, -0.016), (11, -0.027), (12, 0.024), (13, 0.013), (14, 0.027), (15, 0.02), (16, -0.071), (17, 0.011), (18, -0.045), (19, -0.104), (20, 0.044), (21, -0.065), (22, -0.009), (23, 0.001), (24, 0.044), (25, -0.026), (26, -0.05), (27, 0.014), (28, -0.016), (29, -0.02), (30, 0.03), (31, 0.051), (32, -0.021), (33, 0.049), (34, -0.026), (35, -0.054), (36, -0.095), (37, 0.026), (38, -0.016), (39, 0.007), (40, 0.069), (41, -0.017), (42, -0.002), (43, -0.025), (44, -0.024), (45, -0.005), (46, 0.071), (47, 0.042), (48, 0.048), (49, -0.019)]
simIndex simValue blogId blogTitle
same-blog 1 0.96738774 638 andrew gelman stats-2011-03-30-More on the correlation between statistical and political ideology
Introduction: This is a chance for me to combine two of my interests–politics and statistics–and probably to irritate both halves of the readership of this blog. Anyway… I recently wrote about the apparent correlation between Bayes/non-Bayes statistical ideology and liberal/conservative political ideology: The Bayes/non-Bayes fissure had a bit of a political dimension–with anti-Bayesians being the old-line conservatives (for example, Ronald Fisher) and Bayesians having a more of a left-wing flavor (for example, Dennis Lindley). Lots of counterexamples at an individual level, but my impression is that on average the old curmudgeonly, get-off-my-lawn types were (with some notable exceptions) more likely to be anti-Bayesian. This was somewhat based on my experiences at Berkeley. Actually, some of the cranky anti-Bayesians were probably Democrats as well, but when they were being anti-Bayesian they seemed pretty conservative. Recently I received an interesting item from Gerald Cliff, a pro
2 0.70281976 604 andrew gelman stats-2011-03-08-More on the missing conservative psychology researchers
Introduction: Will Wilkinson adds to the discussion of Jonathan Haidt’s remarks regarding the overwhelming prevalance of liberal or left-wing attitudes among psychology professors. I pretty much agree with Wilkinson’s overview: Folks who constantly agree with one another grow insular, self-congratulatory, and not a little lazy. The very possibility of disagreement starts to seem weird or crazy. When you’re trying to do science about human beings, this attitude’s not so great. Wilkinson also reviewed the work of John Jost in this area. Jost is a psychology researcher with the expected liberal/left political leanings, but his relevance here is that he has actually done research on political attitudes and personality types. In Wilkinson’s words: Jost has done plenty of great work that helps explain not only why the best minds in science are liberal, but why most scientists-most academics, even-are liberal. Individuals with the personality trait that most strongly predicts an inclinati
3 0.69815785 639 andrew gelman stats-2011-03-31-Bayes: radical, liberal, or conservative?
Introduction: Radford writes : The word “conservative” gets used many ways, for various political purposes, but I would take it’s basic meaning to be someone who thinks there’s a lot of wisdom in traditional ways of doing things, even if we don’t understand exactly why those ways are good, so we should be reluctant to change unless we have a strong argument that some other way is better. This sounds very Bayesian, with a prior reducing the impact of new data. I agree completely, and I think Radford will very much enjoy my article with Aleks Jakulin , “Bayes: radical, liberal, or conservative?” Radford’s comment also fits with my increasing inclination to use informative prior distributions.
4 0.69609344 961 andrew gelman stats-2011-10-16-The “Washington read” and the algebra of conditional distributions
Introduction: I was trying to explain in class how a (Bayesian) statistician reads the formula for a probability distribution. In old-fashioned statistics textbooks you’re told that if you want to compute a conditional distribution from a joint distribution you need to do some heavy math: p(a|b) = p(a,b)/\int p(a’,b)da’. When doing Bayesian statistics, though, you usually don’t have to do the integration or the division. If you have parameters theta and data y, you first write p(y,theta). Then to get p(theta|y), you don’t need to integrate or divide. All you have to do is look at p(y,theta) in a certain way: Treat y as a constant and theta as a variable. Similarly, if you’re doing the Gibbs sampler and want a conditional distribution, just consider the parameter you’re updating as the variable and everything else as a constant. No need to integrate or divide, you just take the joint distribution and look at it from the right perspective. Awhile ago Yair told me there’s something called
5 0.66410333 2072 andrew gelman stats-2013-10-21-The future (and past) of statistical sciences
Introduction: In connection with this workshop, I was asked to write a few paragraphs describing my perspective on “the current and near-term future state of the statistical sciences you are most familiar with.” Here’s what I wrote: I think that, at any given time, the field of statistics has a core, but that core changes over time. There are different paradigmatic ways to solve problems. 100 or 150 years ago, the thing to do was to identify a phenomenon of interest with some probability distribution and then use the mathematics of that distribution to gain insight into the underlying process. Thus, for example, if certain data looked like they came from a normal distribution, one could surmise that the values in question arose by adding many small independent pieces. If the data looked like they came from a binomial distribution, that would imply independence and equal probabilities. Waiting times that followed an exponential distribution could be considered as coming from a memoryless
6 0.66014564 828 andrew gelman stats-2011-07-28-Thoughts on Groseclose book on media bias
7 0.64963645 1572 andrew gelman stats-2012-11-10-I don’t like this cartoon
8 0.6397751 1560 andrew gelman stats-2012-11-03-Statistical methods that work in some settings but not others
9 0.60880017 1165 andrew gelman stats-2012-02-13-Philosophy of Bayesian statistics: my reactions to Wasserman
10 0.60586923 2201 andrew gelman stats-2014-02-06-Bootstrap averaging: Examples where it works and where it doesn’t work
11 0.60554898 2129 andrew gelman stats-2013-12-10-Cross-validation and Bayesian estimation of tuning parameters
12 0.60500711 1 andrew gelman stats-2010-04-22-Political Belief Networks: Socio-cognitive Heterogeneity in American Public Opinion
13 0.60324985 1042 andrew gelman stats-2011-12-05-Timing is everything!
15 0.59986442 1140 andrew gelman stats-2012-01-27-Educational monoculture
17 0.5995841 877 andrew gelman stats-2011-08-29-Applying quantum probability to political science
18 0.59904063 1633 andrew gelman stats-2012-12-21-Kahan on Pinker on politics
19 0.59669405 2128 andrew gelman stats-2013-12-09-How to model distributions that have outliers in one direction
20 0.59652174 2258 andrew gelman stats-2014-03-21-Random matrices in the news
topicId topicWeight
[(15, 0.016), (16, 0.043), (21, 0.01), (24, 0.089), (27, 0.021), (37, 0.013), (79, 0.028), (86, 0.019), (99, 0.642)]
simIndex simValue blogId blogTitle
same-blog 1 0.99935049 638 andrew gelman stats-2011-03-30-More on the correlation between statistical and political ideology
Introduction: This is a chance for me to combine two of my interests–politics and statistics–and probably to irritate both halves of the readership of this blog. Anyway… I recently wrote about the apparent correlation between Bayes/non-Bayes statistical ideology and liberal/conservative political ideology: The Bayes/non-Bayes fissure had a bit of a political dimension–with anti-Bayesians being the old-line conservatives (for example, Ronald Fisher) and Bayesians having a more of a left-wing flavor (for example, Dennis Lindley). Lots of counterexamples at an individual level, but my impression is that on average the old curmudgeonly, get-off-my-lawn types were (with some notable exceptions) more likely to be anti-Bayesian. This was somewhat based on my experiences at Berkeley. Actually, some of the cranky anti-Bayesians were probably Democrats as well, but when they were being anti-Bayesian they seemed pretty conservative. Recently I received an interesting item from Gerald Cliff, a pro
2 0.99773401 1425 andrew gelman stats-2012-07-23-Examples of the use of hierarchical modeling to generalize to new settings
Introduction: In a link to our back-and-forth on causal inference and the use of hierarchical models to bridge between different inferential settings, Elias Bareinboim (a computer scientist who is working with Judea Pearl) writes : In the past week, I have been engaged in a discussion with Andrew Gelman and his blog readers regarding causal inference, selection bias, confounding, and generalizability. I was trying to understand how his method which he calls “hierarchical modeling” would handle these issues and what guarantees it provides. . . . If anyone understands how “hierarchical modeling” can solve a simple toy problem (e.g., M-bias, control of confounding, mediation, generalizability), please share with us. In his post, Bareinboim raises a direct question about hierarchical modeling and also indirectly brings up larger questions about what is convincing evidence when evaluating a statistical method. As I wrote earlier, Bareinboim believes that “The only way investigators can decide w
Introduction: A colleague recently sent me a copy of some articles on the estimation of treatment interactions (a topic that’s interested me for awhile). One of the articles, which appeared in the Lancet in 2000, was called “ Subgroup analysis and other (mis)uses of baseline data in clinical trials ,” by Susan F. Assmann, Stuart J. Pocock, Laura E. Enos, and Linda E. Kasten. . . . Hey, wait a minute–I know Susan Assmann! Well, I sort of know her. When I was a freshman in college, I asked my adviser, who was an applied math prof, if I could do some research. He connected me to Susan, who was one of his Ph.D. students, and she gave me a tiny part of her thesis to work on. The problem went as follows. You have a function f(x), for x going from 0 to infinity, that is defined as follows. Between 0 and 1, f(x)=x. Then, for x higher than 1, f’(x) = f(x) – f(x-1). The goal is to figure out what f(x) does. I think I’m getting this right here, but I might be getting confused on some of the detai
4 0.99744618 1431 andrew gelman stats-2012-07-27-Overfitting
Introduction: Ilya Esteban writes: In traditional machine learning and statistical learning techniques, you spend a lot of time selecting your input features, fiddling with model parameter values, etc., all of which leads to the problem of overfitting the data and producing overly optimistic estimates for how good the model really is. You can use techniques such as cross-validation and out-of-sample validation data to try to limit the damage, but they are imperfect solutions at best. While Bayesian models have the great advantage of not forcing you to manually select among the various weights and input features, you still often end up trying different priors and model structures (especially with hierarchical models), before coming up with a “final” model. When applying Bayesian modeling to real world data sets, how does should you evaluate alternate priors and topologies for the model without falling into the same overfitting trap as you do with non-Bayesian models? If you try several different
Introduction: I think I’m starting to resolve a puzzle that’s been bugging me for awhile. Pop economists (or, at least, pop micro-economists) are often making one of two arguments: 1. People are rational and respond to incentives. Behavior that looks irrational is actually completely rational once you think like an economist. 2. People are irrational and they need economists, with their open minds, to show them how to be rational and efficient. Argument 1 is associated with “why do they do that?” sorts of puzzles. Why do they charge so much for candy at the movie theater, why are airline ticket prices such a mess, why are people drug addicts, etc. The usual answer is that there’s some rational reason for what seems like silly or self-destructive behavior. Argument 2 is associated with “we can do better” claims such as why we should fire 80% of public-schools teachers or Moneyball-style stories about how some clever entrepreneur has made a zillion dollars by exploiting some inefficienc
6 0.99710059 521 andrew gelman stats-2011-01-17-“the Tea Party’s ire, directed at Democrats and Republicans alike”
8 0.99673402 740 andrew gelman stats-2011-06-01-The “cushy life” of a University of Illinois sociology professor
9 0.99658287 589 andrew gelman stats-2011-02-24-On summarizing a noisy scatterplot with a single comparison of two points
10 0.99651223 1585 andrew gelman stats-2012-11-20-“I know you aren’t the plagiarism police, but . . .”
11 0.9962157 726 andrew gelman stats-2011-05-22-Handling multiple versions of an outcome variable
12 0.9956699 1096 andrew gelman stats-2012-01-02-Graphical communication for legal scholarship
13 0.99553227 772 andrew gelman stats-2011-06-17-Graphical tools for understanding multilevel models
14 0.99541384 756 andrew gelman stats-2011-06-10-Christakis-Fowler update
15 0.99519259 1315 andrew gelman stats-2012-05-12-Question 2 of my final exam for Design and Analysis of Sample Surveys
16 0.99516302 1434 andrew gelman stats-2012-07-29-FindTheData.org
17 0.99501204 604 andrew gelman stats-2011-03-08-More on the missing conservative psychology researchers
18 0.99500328 180 andrew gelman stats-2010-08-03-Climate Change News
19 0.99427497 1536 andrew gelman stats-2012-10-16-Using economics to reduce bike theft
20 0.99425346 1949 andrew gelman stats-2013-07-21-Defensive political science responds defensively to an attack on social science