andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-423 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: John Haubrick writes: Next semester I want to center my statistics class around independent projects that they will present at the end of the semester. My question is, by centering around a project and teaching for the different parts that they need at the time, should topics such as hypothesis testing be moved toward the beginning of the course? Or should I only discuss setting up a research hypothesis and discuss the actual testing later after they have the data? My reply: I’m not sure. There always is a difficulty of what can be covered in a project. My quick thought is that a project will perhaps work better if it is focused on data collection or exploratory data analysis rather than on estimation and hypothesis testing, which are topics that get covered pretty well in the course as a whole.
sentIndex sentText sentNum sentScore
1 John Haubrick writes: Next semester I want to center my statistics class around independent projects that they will present at the end of the semester. [sent-1, score-1.109]
2 My question is, by centering around a project and teaching for the different parts that they need at the time, should topics such as hypothesis testing be moved toward the beginning of the course? [sent-2, score-2.335]
3 Or should I only discuss setting up a research hypothesis and discuss the actual testing later after they have the data? [sent-3, score-1.428]
4 There always is a difficulty of what can be covered in a project. [sent-5, score-0.51]
5 My quick thought is that a project will perhaps work better if it is focused on data collection or exploratory data analysis rather than on estimation and hypothesis testing, which are topics that get covered pretty well in the course as a whole. [sent-6, score-2.559]
wordName wordTfidf (topN-words)
[('testing', 0.353), ('covered', 0.32), ('hypothesis', 0.314), ('centering', 0.259), ('topics', 0.239), ('project', 0.221), ('discuss', 0.197), ('semester', 0.171), ('exploratory', 0.148), ('projects', 0.147), ('course', 0.147), ('around', 0.147), ('beginning', 0.143), ('collection', 0.143), ('focused', 0.135), ('moved', 0.132), ('parts', 0.131), ('independent', 0.124), ('difficulty', 0.123), ('center', 0.123), ('estimation', 0.122), ('setting', 0.113), ('teaching', 0.111), ('toward', 0.107), ('present', 0.106), ('quick', 0.106), ('data', 0.105), ('actual', 0.104), ('later', 0.099), ('class', 0.096), ('whole', 0.095), ('john', 0.092), ('end', 0.088), ('next', 0.086), ('reply', 0.075), ('always', 0.067), ('need', 0.066), ('perhaps', 0.065), ('thought', 0.064), ('pretty', 0.063), ('question', 0.061), ('better', 0.056), ('analysis', 0.055), ('statistics', 0.054), ('well', 0.054), ('rather', 0.053), ('want', 0.053), ('different', 0.051), ('research', 0.051), ('work', 0.044)]
simIndex simValue blogId blogTitle
same-blog 1 0.99999994 423 andrew gelman stats-2010-11-20-How to schedule projects in an introductory statistics course?
Introduction: John Haubrick writes: Next semester I want to center my statistics class around independent projects that they will present at the end of the semester. My question is, by centering around a project and teaching for the different parts that they need at the time, should topics such as hypothesis testing be moved toward the beginning of the course? Or should I only discuss setting up a research hypothesis and discuss the actual testing later after they have the data? My reply: I’m not sure. There always is a difficulty of what can be covered in a project. My quick thought is that a project will perhaps work better if it is focused on data collection or exploratory data analysis rather than on estimation and hypothesis testing, which are topics that get covered pretty well in the course as a whole.
2 0.2145519 1605 andrew gelman stats-2012-12-04-Write This Book
Introduction: This post is by Phil Price. I’ve been preparing a review of a new statistics textbook aimed at students and practitioners in the “physical sciences,” as distinct from the social sciences and also distinct from people who intend to take more statistics courses. I figured that since it’s been years since I looked at an intro stats textbook, I should look at a few others and see how they differ from this one, so in addition to the book I’m reviewing I’ve looked at some other textbooks aimed at similar audiences: Milton and Arnold; Hines, Montgomery, Goldsman, and Borror; and a few others. I also looked at the table of contents of several more. There is a lot of overlap in the coverage of these books — they all have discussions of common discrete and continuous distributions, joint distributions, descriptive statistics, parameter estimation, hypothesis testing, linear regression, ANOVA, factorial experimental design, and a few other topics. I can see how, from a statisti
3 0.18760931 1024 andrew gelman stats-2011-11-23-Of hypothesis tests and Unitarians
Introduction: Xian, Judith, and I read this line in a book by statistician Murray Aitkin in which he considered the following hypothetical example: A survey of 100 individuals expressing support (Yes/No) for the president, before and after a presidential address . . . The question of interest is whether there has been a change in support between the surveys . . . We want to assess the evidence for the hypothesis of equality H1 against the alternative hypothesis H2 of a change. Here is our response : Based on our experience in public opinion research, this is not a real question. Support for any political position is always changing. The real question is how much the support has changed, or perhaps how this change is distributed across the population. A defender of Aitkin (and of classical hypothesis testing) might respond at this point that, yes, everybody knows that changes are never exactly zero and that we should take a more “grown-up” view of the null hypothesis, not that the change
4 0.17294835 2263 andrew gelman stats-2014-03-24-Empirical implications of Empirical Implications of Theoretical Models
Introduction: Robert Bloomfield writes: Most of the people in my field (accounting, which is basically applied economics and finance, leavened with psychology and organizational behavior) use ‘positive research methods’, which are typically described as coming to the data with a predefined theory, and using hypothesis testing to accept or reject the theory’s predictions. But a substantial minority use ‘interpretive research methods’ (sometimes called qualitative methods, for those that call positive research ‘quantitative’). No one seems entirely happy with the definition of this method, but I’ve found it useful to think of it as an attempt to see the world through the eyes of your subjects, much as Jane Goodall lived with gorillas and tried to see the world through their eyes.) Interpretive researchers often criticize positive researchers by noting that the latter don’t make the best use of their data, because they come to the data with a predetermined theory, and only test a narrow set of h
5 0.17015749 1582 andrew gelman stats-2012-11-18-How to teach methods we don’t like?
Introduction: April Galyardt writes: I’m teaching my first graduate class this semester. It’s intro stats for graduate students in the college of education. Most of the students are first year PhD students. Though, there are a number of master’s students who are primarily in-service teachers. The difficulties with teaching an undergraduate intro stats course are still present, in that mathematical preparation and phobia vary widely across the class. I’ve been enjoying the class and the students, but I’d like your take on an issue I’ve been thinking about. How do I balance teaching the standard methods, like hypothesis testing, that these future researchers have to know because they are so standard, with discussing the problems with those methods (e.g. p-value as a measure of sample size , and the decline effect , not to mention multiple testing and other common mistakes). It feels a bit like saying “Ok here’s what everybody does, but really it’s broken” and then there’s not enough time to tal
6 0.16813323 2183 andrew gelman stats-2014-01-23-Discussion on preregistration of research studies
7 0.15376405 1016 andrew gelman stats-2011-11-17-I got 99 comparisons but multiplicity ain’t one
9 0.14322132 2295 andrew gelman stats-2014-04-18-One-tailed or two-tailed?
10 0.12074599 2281 andrew gelman stats-2014-04-04-The Notorious N.H.S.T. presents: Mo P-values Mo Problems
11 0.11607493 1869 andrew gelman stats-2013-05-24-In which I side with Neyman over Fisher
12 0.11591421 2009 andrew gelman stats-2013-09-05-A locally organized online BDA course on G+ hangout?
13 0.11549105 1611 andrew gelman stats-2012-12-07-Feedback on my Bayesian Data Analysis class at Columbia
14 0.11400527 1695 andrew gelman stats-2013-01-28-Economists argue about Bayes
15 0.11382279 1355 andrew gelman stats-2012-05-31-Lindley’s paradox
16 0.11228334 1668 andrew gelman stats-2013-01-11-My talk at the NY data visualization meetup this Monday!
18 0.11205941 1450 andrew gelman stats-2012-08-08-My upcoming talk for the data visualization meetup
19 0.10781135 2102 andrew gelman stats-2013-11-15-“Are all significant p-values created equal?”
20 0.10756634 1517 andrew gelman stats-2012-10-01-“On Inspiring Students and Being Human”
topicId topicWeight
[(0, 0.162), (1, 0.016), (2, -0.064), (3, -0.023), (4, 0.029), (5, 0.07), (6, -0.057), (7, 0.085), (8, -0.012), (9, -0.055), (10, -0.027), (11, 0.054), (12, 0.053), (13, -0.121), (14, 0.035), (15, -0.059), (16, -0.073), (17, -0.063), (18, 0.013), (19, -0.058), (20, 0.059), (21, 0.011), (22, -0.025), (23, -0.014), (24, -0.111), (25, -0.081), (26, 0.036), (27, -0.09), (28, 0.059), (29, -0.006), (30, 0.045), (31, -0.069), (32, -0.001), (33, -0.015), (34, -0.097), (35, -0.012), (36, 0.015), (37, -0.02), (38, 0.041), (39, 0.052), (40, -0.043), (41, -0.015), (42, -0.012), (43, -0.04), (44, -0.01), (45, 0.069), (46, 0.02), (47, -0.068), (48, -0.025), (49, -0.03)]
simIndex simValue blogId blogTitle
same-blog 1 0.97154683 423 andrew gelman stats-2010-11-20-How to schedule projects in an introductory statistics course?
Introduction: John Haubrick writes: Next semester I want to center my statistics class around independent projects that they will present at the end of the semester. My question is, by centering around a project and teaching for the different parts that they need at the time, should topics such as hypothesis testing be moved toward the beginning of the course? Or should I only discuss setting up a research hypothesis and discuss the actual testing later after they have the data? My reply: I’m not sure. There always is a difficulty of what can be covered in a project. My quick thought is that a project will perhaps work better if it is focused on data collection or exploratory data analysis rather than on estimation and hypothesis testing, which are topics that get covered pretty well in the course as a whole.
2 0.73742938 1024 andrew gelman stats-2011-11-23-Of hypothesis tests and Unitarians
Introduction: Xian, Judith, and I read this line in a book by statistician Murray Aitkin in which he considered the following hypothetical example: A survey of 100 individuals expressing support (Yes/No) for the president, before and after a presidential address . . . The question of interest is whether there has been a change in support between the surveys . . . We want to assess the evidence for the hypothesis of equality H1 against the alternative hypothesis H2 of a change. Here is our response : Based on our experience in public opinion research, this is not a real question. Support for any political position is always changing. The real question is how much the support has changed, or perhaps how this change is distributed across the population. A defender of Aitkin (and of classical hypothesis testing) might respond at this point that, yes, everybody knows that changes are never exactly zero and that we should take a more “grown-up” view of the null hypothesis, not that the change
Introduction: Masanao sends this one in, under the heading, “another incident of misunderstood p-value”: Warren Davies, a positive psychology MSc student at UEL, provides the latest in our ongoing series of guest features for students. Warren has just released a Psychology Study Guide, which covers information on statistics, research methods and study skills for psychology students. Despite the myriad rules and procedures of science, some research findings are pure flukes. Perhaps you’re testing a new drug, and by chance alone, a large number of people spontaneously get better. The better your study is conducted, the lower the chance that your result was a fluke – but still, there is always a certain probability that it was. Statistical significance testing gives you an idea of what this probability is. In science we’re always testing hypotheses. We never conduct a study to ‘see what happens’, because there’s always at least one way to make any useless set of data look important. We take
4 0.70821118 2263 andrew gelman stats-2014-03-24-Empirical implications of Empirical Implications of Theoretical Models
Introduction: Robert Bloomfield writes: Most of the people in my field (accounting, which is basically applied economics and finance, leavened with psychology and organizational behavior) use ‘positive research methods’, which are typically described as coming to the data with a predefined theory, and using hypothesis testing to accept or reject the theory’s predictions. But a substantial minority use ‘interpretive research methods’ (sometimes called qualitative methods, for those that call positive research ‘quantitative’). No one seems entirely happy with the definition of this method, but I’ve found it useful to think of it as an attempt to see the world through the eyes of your subjects, much as Jane Goodall lived with gorillas and tried to see the world through their eyes.) Interpretive researchers often criticize positive researchers by noting that the latter don’t make the best use of their data, because they come to the data with a predetermined theory, and only test a narrow set of h
5 0.69015431 1605 andrew gelman stats-2012-12-04-Write This Book
Introduction: This post is by Phil Price. I’ve been preparing a review of a new statistics textbook aimed at students and practitioners in the “physical sciences,” as distinct from the social sciences and also distinct from people who intend to take more statistics courses. I figured that since it’s been years since I looked at an intro stats textbook, I should look at a few others and see how they differ from this one, so in addition to the book I’m reviewing I’ve looked at some other textbooks aimed at similar audiences: Milton and Arnold; Hines, Montgomery, Goldsman, and Borror; and a few others. I also looked at the table of contents of several more. There is a lot of overlap in the coverage of these books — they all have discussions of common discrete and continuous distributions, joint distributions, descriptive statistics, parameter estimation, hypothesis testing, linear regression, ANOVA, factorial experimental design, and a few other topics. I can see how, from a statisti
6 0.68640703 2295 andrew gelman stats-2014-04-18-One-tailed or two-tailed?
7 0.68086189 2183 andrew gelman stats-2014-01-23-Discussion on preregistration of research studies
8 0.65715307 2309 andrew gelman stats-2014-04-28-Crowdstorming a dataset
9 0.65592211 1582 andrew gelman stats-2012-11-18-How to teach methods we don’t like?
10 0.64501059 1355 andrew gelman stats-2012-05-31-Lindley’s paradox
11 0.6342991 2281 andrew gelman stats-2014-04-04-The Notorious N.H.S.T. presents: Mo P-values Mo Problems
12 0.62834156 1869 andrew gelman stats-2013-05-24-In which I side with Neyman over Fisher
13 0.60723871 2127 andrew gelman stats-2013-12-08-The never-ending (and often productive) race between theory and practice
14 0.60523492 1776 andrew gelman stats-2013-03-25-The harm done by tests of significance
15 0.59870571 544 andrew gelman stats-2011-01-29-Splitting the data
16 0.59520918 1722 andrew gelman stats-2013-02-14-Statistics for firefighters: update
17 0.59247983 2016 andrew gelman stats-2013-09-11-Zipfian Academy, A School for Data Science
18 0.58324808 1517 andrew gelman stats-2012-10-01-“On Inspiring Students and Being Human”
19 0.58296138 799 andrew gelman stats-2011-07-13-Hypothesis testing with multiple imputations
20 0.57932401 2345 andrew gelman stats-2014-05-24-An interesting mosaic of a data programming course
topicId topicWeight
[(15, 0.022), (16, 0.02), (21, 0.038), (24, 0.179), (28, 0.102), (86, 0.054), (99, 0.456)]
simIndex simValue blogId blogTitle
same-blog 1 0.98415446 423 andrew gelman stats-2010-11-20-How to schedule projects in an introductory statistics course?
Introduction: John Haubrick writes: Next semester I want to center my statistics class around independent projects that they will present at the end of the semester. My question is, by centering around a project and teaching for the different parts that they need at the time, should topics such as hypothesis testing be moved toward the beginning of the course? Or should I only discuss setting up a research hypothesis and discuss the actual testing later after they have the data? My reply: I’m not sure. There always is a difficulty of what can be covered in a project. My quick thought is that a project will perhaps work better if it is focused on data collection or exploratory data analysis rather than on estimation and hypothesis testing, which are topics that get covered pretty well in the course as a whole.
2 0.98006952 166 andrew gelman stats-2010-07-27-The Three Golden Rules for Successful Scientific Research
Introduction: A famous computer scientist, Edsger W. Dijkstra, was writing short memos on a daily basis for most of his life. His memo archives contains a little over 1300 memos. I guess today he would be writing a blog, although his memos do tend to be slightly more profound than what I post. Here are the rules (follow link for commentary), which I tried to summarize: Pursue quality and challenge, avoid routine. (“Raise your quality standards as high as you can live with, avoid wasting your time on routine problems, and always try to work as closely as possible at the boundary of your abilities. Do this, because it is the only way of discovering how that boundary should be moved forward.”) When pursuing social relevance, never compromise on scientific soundness. (“We all like our work to be socially relevant and scientifically sound. If we can find a topic satisfying both desires, we are lucky; if the two targets are in conflict with each other, let the requirement of scientific sou
3 0.97938812 835 andrew gelman stats-2011-08-02-“The sky is the limit” isn’t such a good thing
Introduction: A few months ago, in response to some reflections from Steve Hsu, I wrote the following: I’ve long been glad that when I went to college in the 80s, I was at MIT rather than Harvard. Either place I would’ve taken hard classes and learned a lot, but one advantage of MIT was that we had no sense–no sense at all–that we could make big bucks. We had no sense of making moderately big bucks as lawyers, no sense of making big bucks working on Wall Street, and no sense of making really big bucks by starting a business. I mean, sure, we knew about lawyers (but we didn’t know that a lawyer with technical skills would be a killer combination), we knew about Wall Street (but we had no idea what they did, other than shout pork belly prices across a big room), and we knew about tech startups (but we had no idea that they were anything to us beyond a source of jobs for engineers). What we were all looking for was a good solid job with cool benefits (like those companies in California that had gy
4 0.97864497 1719 andrew gelman stats-2013-02-11-Why waste time philosophizing?
Introduction: I’ll answer the above question after first sharing some background and history on the the philosophy of Bayesian statistics, which appeared at the end of our rejoinder to the discussion to which I linked the other day: When we were beginning our statistical educations, the word ‘Bayesian’ conveyed membership in an obscure cult. Statisticians who were outside the charmed circle could ignore the Bayesian subfield, while Bayesians themselves tended to be either apologetic or brazenly defiant. These two extremes manifested themselves in ever more elaborate proposals for non-informative priors, on the one hand, and declarations of the purity of subjective probability, on the other. Much has changed in the past 30 years. ‘Bayesian’ is now often used in casual scientific parlance as a synonym for ‘rational’, the anti-Bayesians have mostly disappeared, and non-Bayesian statisticians feel the need to keep up with developments in Bayesian modelling and computation. Bayesians themselves
5 0.97606301 1228 andrew gelman stats-2012-03-25-Continuous variables in Bayesian networks
Introduction: Antti Rasinen writes: I’m a former undergrad machine learning student and a current software engineer with a Bayesian hobby. Today my two worlds collided. I ask for some enlightenment. On your blog you’ve repeatedly advocated continuous distributions with Bayesian models. Today I read this article by Ricky Ho, who writes: The strength of Bayesian network is it is highly scalable and can learn incrementally because all we do is to count the observed variables and update the probability distribution table. Similar to Neural Network, Bayesian network expects all data to be binary, categorical variable will need to be transformed into multiple binary variable as described above. Numeric variable is generally not a good fit for Bayesian network. The last sentence seems to be at odds with what you’ve said. Sadly, I don’t have enough expertise to say which view of the world is correct. During my undergrad years our team wrote an implementation of the Junction Tree algorithm. We r
7 0.97500277 295 andrew gelman stats-2010-09-25-Clusters with very small numbers of observations
8 0.97441828 754 andrew gelman stats-2011-06-09-Difficulties with Bayesian model averaging
9 0.97408271 1630 andrew gelman stats-2012-12-18-Postdoc positions at Microsoft Research – NYC
10 0.97385204 852 andrew gelman stats-2011-08-13-Checking your model using fake data
11 0.97332674 1812 andrew gelman stats-2013-04-19-Chomsky chomsky chomsky chomsky furiously
12 0.97327149 505 andrew gelman stats-2011-01-05-Wacky interview questions: An exploration into the nature of evidence on the internet
13 0.97299248 957 andrew gelman stats-2011-10-14-Questions about a study of charter schools
14 0.9725005 1482 andrew gelman stats-2012-09-04-Model checking and model understanding in machine learning
15 0.97244316 395 andrew gelman stats-2010-11-05-Consulting: how do you figure out what to charge?
16 0.97242451 2176 andrew gelman stats-2014-01-19-Transformations for non-normal data
17 0.97240174 1611 andrew gelman stats-2012-12-07-Feedback on my Bayesian Data Analysis class at Columbia
18 0.97176027 731 andrew gelman stats-2011-05-26-Lottery probability update
19 0.97156298 2364 andrew gelman stats-2014-06-08-Regression and causality and variable ordering
20 0.97128552 2041 andrew gelman stats-2013-09-27-Setting up Jitts online