andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2147 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: Anaface analysis of Michelangelo’s David I’ve come across a paper that was using “beauty” as one of the predictors. To measure beauty, the authors used Anaface.com I don’t trust metrics without trying them on a gold standard first. So, I tried how well Anaface does on something that the arts world considers as one of gold standards of beauty – Michelangelo’s David. My annotation might be imperfect, but David only gets to be only a good 7: his nose is too narrow and his eyes are too close. Of course, I applaud the use of interesting predictors in studies, and Anaface is a better tool than anything I’ve seen before, but maybe we need better metrics! What do you think?
sentIndex sentText sentNum sentScore
1 Anaface analysis of Michelangelo’s David I’ve come across a paper that was using “beauty” as one of the predictors. [sent-1, score-0.262]
2 com I don’t trust metrics without trying them on a gold standard first. [sent-3, score-0.786]
3 So, I tried how well Anaface does on something that the arts world considers as one of gold standards of beauty – Michelangelo’s David. [sent-4, score-1.205]
4 My annotation might be imperfect, but David only gets to be only a good 7: his nose is too narrow and his eyes are too close. [sent-5, score-0.367]
5 Of course, I applaud the use of interesting predictors in studies, and Anaface is a better tool than anything I’ve seen before, but maybe we need better metrics! [sent-6, score-0.743]
wordName wordTfidf (topN-words)
[('anaface', 0.589), ('michelangelo', 0.392), ('beauty', 0.358), ('metrics', 0.287), ('gold', 0.256), ('applaud', 0.133), ('david', 0.131), ('arts', 0.128), ('imperfect', 0.126), ('considers', 0.126), ('eyes', 0.123), ('narrow', 0.113), ('standards', 0.099), ('tool', 0.092), ('trust', 0.088), ('predictors', 0.084), ('better', 0.078), ('tried', 0.077), ('measure', 0.074), ('gets', 0.07), ('authors', 0.066), ('across', 0.063), ('seen', 0.063), ('studies', 0.061), ('ve', 0.06), ('standard', 0.056), ('world', 0.054), ('trying', 0.052), ('come', 0.052), ('course', 0.051), ('anything', 0.051), ('without', 0.047), ('interesting', 0.046), ('need', 0.045), ('used', 0.043), ('maybe', 0.039), ('analysis', 0.038), ('well', 0.037), ('using', 0.037), ('paper', 0.037), ('one', 0.035), ('something', 0.035), ('use', 0.034), ('might', 0.032), ('good', 0.029), ('think', 0.019)]
simIndex simValue blogId blogTitle
same-blog 1 1.0000001 2147 andrew gelman stats-2013-12-25-Measuring Beauty
Introduction: Anaface analysis of Michelangelo’s David I’ve come across a paper that was using “beauty” as one of the predictors. To measure beauty, the authors used Anaface.com I don’t trust metrics without trying them on a gold standard first. So, I tried how well Anaface does on something that the arts world considers as one of gold standards of beauty – Michelangelo’s David. My annotation might be imperfect, but David only gets to be only a good 7: his nose is too narrow and his eyes are too close. Of course, I applaud the use of interesting predictors in studies, and Anaface is a better tool than anything I’ve seen before, but maybe we need better metrics! What do you think?
2 0.10664834 1513 andrew gelman stats-2012-09-27-Estimating seasonality with a data set that’s just 52 weeks long
Introduction: Kaiser asks: Trying to figure out what are some keywords to research for this problem I’m trying to solve. I need to estimate seasonality but without historical data. What I have are multiple time series of correlated metrics (think department store sales, movie receipts, etc.) but all of them for 52 weeks only. I’m thinking that if these metrics are all subject to some underlying seasonality, I should be able to estimate that without needing prior years data. My reply: Can I blog this and see if the hive mind responds? I’m not an expert on this one. My first thought is to fit an additive model including date effects, with some sort of spline on the date effects along with day-of-week effects, idiosyncratic date effects (July 4th, Christmas, etc.), and possible interactions. Actually, I’d love to fit something like that in Stan, just to see how it turns out. It could be a tangled mess but it could end up working really well!
3 0.10222253 680 andrew gelman stats-2011-04-26-My talk at Berkeley on Wednesday
Introduction: Something on Applied Bayesian Statistics April 27, 4:10-5 p.m., 1011 Evans Hall I will deliver one of the following three talks: 1. Of beauty, sex, and power: Statistical challenges in estimating small effects 2. Why we (usually) don’t worry about multiple comparisons 3. Parameterization and Bayesian modeling Whoever shows up on time to the seminar gets to vote, and I’ll give the talk that gets the most votes.
4 0.10125531 679 andrew gelman stats-2011-04-25-My talk at Stanford on Tuesday
Introduction: Of Beauty, Sex, and Power: Statistical Challenges in Estimating Small Effects. Tues 26 Apr, 12-1 in the Graham Stuart Lounge, 4th Floor, Encina West.
5 0.09501911 785 andrew gelman stats-2011-07-02-Experimental reasoning in social science
Introduction: As a statistician, I was trained to think of randomized experimentation as representing the gold standard of knowledge in the social sciences, and, despite having seen occasional arguments to the contrary, I still hold that view, expressed pithily by Box, Hunter, and Hunter (1978) that “To find out what happens when you change something, it is necessary to change it.” At the same time, in my capacity as a social scientist, I’ve published many applied research papers, almost none of which have used experimental data. In the present article, I’ll address the following questions: 1. Why do I agree with the consensus characterization of randomized experimentation as a gold standard? 2. Given point 1 above, why does almost all my research use observational data? In confronting these issues, we must consider some general issues in the strategy of social science research. We also take from the psychology methods literature a more nuanced perspective that considers several differen
6 0.080549911 694 andrew gelman stats-2011-05-04-My talk at Hunter College on Thurs
7 0.070640802 651 andrew gelman stats-2011-04-06-My talk at Northwestern University tomorrow (Thursday)
8 0.070244819 244 andrew gelman stats-2010-08-30-Useful models, model checking, and external validation: a mini-discussion
9 0.070227228 1013 andrew gelman stats-2011-11-16-My talk at Math for America on Saturday
10 0.064386532 1718 andrew gelman stats-2013-02-11-Toward a framework for automatic model building
11 0.057035215 368 andrew gelman stats-2010-10-25-Is instrumental variables analysis particularly susceptible to Type M errors?
12 0.055670254 1966 andrew gelman stats-2013-08-03-Uncertainty in parameter estimates using multilevel models
13 0.054624811 446 andrew gelman stats-2010-12-03-Is 0.05 too strict as a p-value threshold?
14 0.053248949 1778 andrew gelman stats-2013-03-27-My talk at the University of Michigan today 4pm
15 0.052233301 1426 andrew gelman stats-2012-07-23-Special effects
16 0.05204317 2286 andrew gelman stats-2014-04-08-Understanding Simpson’s paradox using a graph
17 0.049566448 2345 andrew gelman stats-2014-05-24-An interesting mosaic of a data programming course
18 0.048212104 798 andrew gelman stats-2011-07-12-Sometimes a graph really is just ugly
19 0.047567464 1811 andrew gelman stats-2013-04-18-Psychology experiments to understand what’s going on with data graphics?
20 0.046326675 1640 andrew gelman stats-2012-12-26-What do people do wrong? WSJ columnist is looking for examples!
topicId topicWeight
[(0, 0.078), (1, 0.0), (2, -0.005), (3, -0.016), (4, 0.02), (5, -0.01), (6, 0.005), (7, 0.003), (8, 0.001), (9, 0.016), (10, 0.013), (11, 0.006), (12, 0.005), (13, 0.0), (14, 0.0), (15, -0.009), (16, -0.017), (17, 0.011), (18, -0.007), (19, 0.011), (20, -0.02), (21, -0.006), (22, -0.009), (23, 0.01), (24, 0.003), (25, -0.003), (26, -0.01), (27, -0.013), (28, 0.018), (29, 0.004), (30, 0.031), (31, -0.012), (32, 0.005), (33, -0.021), (34, 0.011), (35, 0.007), (36, 0.001), (37, -0.009), (38, -0.005), (39, -0.011), (40, 0.02), (41, 0.001), (42, 0.009), (43, 0.009), (44, -0.011), (45, -0.016), (46, 0.033), (47, 0.023), (48, 0.029), (49, -0.034)]
simIndex simValue blogId blogTitle
same-blog 1 0.93941224 2147 andrew gelman stats-2013-12-25-Measuring Beauty
Introduction: Anaface analysis of Michelangelo’s David I’ve come across a paper that was using “beauty” as one of the predictors. To measure beauty, the authors used Anaface.com I don’t trust metrics without trying them on a gold standard first. So, I tried how well Anaface does on something that the arts world considers as one of gold standards of beauty – Michelangelo’s David. My annotation might be imperfect, but David only gets to be only a good 7: his nose is too narrow and his eyes are too close. Of course, I applaud the use of interesting predictors in studies, and Anaface is a better tool than anything I’ve seen before, but maybe we need better metrics! What do you think?
2 0.69718385 490 andrew gelman stats-2010-12-29-Brain Structure and the Big Five
Introduction: Many years ago, a research psychologist whose judgment I greatly respect told me that the characterization of personality by the so-called Big Five traits (extraversion, etc.) was old-fashioned. So I’m always surprised to see that the Big Five keeps cropping up. I guess not everyone agrees that it’s a bad idea. For example, Hamdan Azhar wrote to me: I was wondering if you’d seen this recent paper (De Young et al. 2010) that finds significant correlations between brain volume in selected regions and personality trait measures (from the Big Five). This is quite a ground-breaking finding and it was covered extensively in the mainstream media. I think readers of your blog would be interested in your thoughts, statistically speaking, on their methodology and findings. My reply: I’d be interested in my thoughts on this too! But I don’t know enough to say anything useful. From the abstract of the paper under discussion: Controlling for age, sex, and whole-brain volume
3 0.66673744 1747 andrew gelman stats-2013-03-03-More research on the role of puzzles in processing data graphics
Introduction: Ruth Rosenholtz of the department of Brain and Cognitive Science at MIT writes: We mostly do computational modeling of human vision. We try to do on the one hand the sort of basic science that fits in the human vision community, while on the other hand developing predictive models which might actually lend insight into design. Your talk resonated with me in part because of this paper [Do Predictions of Visual Perception Aid Design?, by Ruth Rosenholtz, Amal Dorai, and Rosalind Freeman]. We went into our study thinking that people would like to have a quantitative tool to help analyze designs. But what we concluded, somewhat anecdotally, was that its main use seemed to be as a conversation-starter, and a means of communicating ideas about the design. And the reason it seemed to work is that our visualizations were the right level of a “puzzle” — challenging enough to be a bit fun to work out. On another topic, check out the infographic from last weekend’s NYTimes ma
Introduction: Please answer the above question before reading on . . . I’m curious after reading Leif Nelson’s report that, based on research with Minah Jung, approximately 42% of the people they surveyed said they bought laundry detergent on their most recent trip to the store. I’m stunned that the number is so high. 42%??? That’s almost half the time. If we bought laundry detergent half the time we went to the store, our apartment would be stacked so full with the stuff, we wouldn’t be able to enter the door. I think we buy laundry detergent . . . ummm, how often? There are 40 of those little laundry packets in the box, we do laundry once a day, sometimes twice, let’s say 10 times a week, so this means we buy detergent about once every 4 weeks. We go to the store, hmmm, about once a day, let’s say 5 times a week to put our guess on the conservative side. So, 20 trips to the store for each purchase of detergent, that’s 5% of the time. Compared to us, lots of people must (a) go to
5 0.65951651 446 andrew gelman stats-2010-12-03-Is 0.05 too strict as a p-value threshold?
Introduction: Seth sent along an article (not by him) from the psychology literature and wrote: This is a good example of your complaint about statistical significance. The authors want to say that predictability of information determines how distracting something is and have two conditions that vary in predictability. One is significantly distracting, the other isn’t. But the two conditions are not significantly different from each other. So the two conditions are different more weakly than p = 0.05. I don’t think the reviewers failed to notice this. They just thought it should be published anyway, is my guess. To me, the interesting question is: where should the bar be? at p = 0.05? at p = 0.10? something else? How can we figure out where to put the bar? I replied: My quick answer is that we have to get away from .05 and .10 and move to something that takes into account prior information. This could be Bayesian (of course) or could be done classically using power calculations, as disc
6 0.65644377 752 andrew gelman stats-2011-06-08-Traffic Prediction
9 0.64705473 685 andrew gelman stats-2011-04-29-Data mining and allergies
10 0.64696693 1971 andrew gelman stats-2013-08-07-I doubt they cheated
12 0.64544511 726 andrew gelman stats-2011-05-22-Handling multiple versions of an outcome variable
13 0.64541596 1289 andrew gelman stats-2012-04-29-We go to war with the data we have, not the data we want
14 0.64141202 2090 andrew gelman stats-2013-11-05-How much do we trust a new claim that early childhood stimulation raised earnings by 42%?
15 0.64044625 36 andrew gelman stats-2010-05-16-Female Mass Murderers: Babes Behind Bars
16 0.63567847 1114 andrew gelman stats-2012-01-12-Controversy about average personality differences between men and women
17 0.63452107 303 andrew gelman stats-2010-09-28-“Genomics” vs. genetics
18 0.63235122 1718 andrew gelman stats-2013-02-11-Toward a framework for automatic model building
topicId topicWeight
[(16, 0.022), (23, 0.053), (24, 0.06), (33, 0.07), (48, 0.225), (65, 0.024), (66, 0.085), (76, 0.024), (86, 0.015), (99, 0.262)]
simIndex simValue blogId blogTitle
1 0.92223895 2363 andrew gelman stats-2014-06-07-“Does researching casual marijuana use cause brain abnormalities?”
Introduction: David Austin points me to a wonderfully-titled post by Lior Pachter criticizing a recent paper on the purported effects of cannabis use. Not the paper criticized here . Someone should send this all to David Brooks. I’ve heard he’s interested in the latest scientific findings, and I know he’s interested in marijuana.
2 0.91196805 332 andrew gelman stats-2010-10-10-Proposed new section of the American Statistical Association on Imaging Sciences
Introduction: Martin Lindquist writes that he and others are trying to start a new ASA section on statistics in imaging. If you’re interested in being a signatory to its formation, please send him an email.
3 0.89181554 1088 andrew gelman stats-2011-12-28-Argument in favor of Ddulites
Introduction: Mark Palko defines a Ddulite as follows: A preference for higher tech solutions even in cases where lower tech alternatives have greater and more appropriate functionality; a person of ddulite tendencies. Though Ddulites are the opposite of Luddites with respect to attitudes toward technology, they occupy more or less the same point with respect to functionality. As a sometime Luddite myself (no cell phone, tv, microwave oven, etc.), I should in fairness point out the logic in favor of being a Ddulite. Old technology is typically pretty stable; new technology is improving. It can make sense to switch early (before the new technology actually performs better than the old) to get the benefits of being familiar with the new technology once it does take off.
same-blog 4 0.8891874 2147 andrew gelman stats-2013-12-25-Measuring Beauty
Introduction: Anaface analysis of Michelangelo’s David I’ve come across a paper that was using “beauty” as one of the predictors. To measure beauty, the authors used Anaface.com I don’t trust metrics without trying them on a gold standard first. So, I tried how well Anaface does on something that the arts world considers as one of gold standards of beauty – Michelangelo’s David. My annotation might be imperfect, but David only gets to be only a good 7: his nose is too narrow and his eyes are too close. Of course, I applaud the use of interesting predictors in studies, and Anaface is a better tool than anything I’ve seen before, but maybe we need better metrics! What do you think?
5 0.8730697 841 andrew gelman stats-2011-08-06-Twitteo killed the bloggio star . . . Not!
Introduction: Alex Braunstein writes: Thanks for the post . You drove >800 pageviews to my site. That’s >90% of what Robert Scoble’s tweet generated with 184k followers, which I find incredibly impressive. 800 doesn’t sound like so much to me, but I suppose if it’s the right 800 . . .
7 0.85600281 2126 andrew gelman stats-2013-12-07-If I could’ve done it all over again
8 0.83879411 202 andrew gelman stats-2010-08-12-Job openings in multilevel modeling in Bristol, England
9 0.83678067 181 andrew gelman stats-2010-08-03-MCMC in Python
10 0.82935274 212 andrew gelman stats-2010-08-17-Futures contracts, Granger causality, and my preference for estimation to testing
12 0.8092097 1496 andrew gelman stats-2012-09-14-Sides and Vavreck on the 2012 election
13 0.78603959 1234 andrew gelman stats-2012-03-28-The Supreme Court’s Many Median Justices
14 0.7839554 823 andrew gelman stats-2011-07-26-Including interactions or not
15 0.78228223 976 andrew gelman stats-2011-10-27-Geophysicist Discovers Modeling Error (in Economics)
16 0.7726438 1038 andrew gelman stats-2011-12-02-Donate Your Data to Science!
17 0.76704085 316 andrew gelman stats-2010-10-03-Suggested reading for a prospective statistician?
18 0.75987148 605 andrew gelman stats-2011-03-09-Does it feel like cheating when I do this? Variation in ethical standards and expectations
19 0.75858498 681 andrew gelman stats-2011-04-26-Worst statistical graphic I have seen this year
20 0.75322753 464 andrew gelman stats-2010-12-12-Finite-population standard deviation in a hierarchical model