andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1681 knowledge-graph by maker-knowledge-mining
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Introduction: Richard Morey writes: Rink Hoekstra and I are undertaking some research to explore how people use classical statistical results to evaluate the weight of evidence. Bayesians often critique classical techniques for being difficult to interpret in terms of what scientists want to know, but there is clearly information in the statistics themselves. We wonder how people extract that information. Below is our official announcement; it would be great if you could let people on your blog know about the survey, as we want to get a wide variety of statistical users to take the survey. Announcement follows: Empirical science is grounded on the belief that data can be used as evidence. The convincingness of data — the “weight” of the evidence they provide — is crucial to deciding between rival scientific positions. In situations with no uncertainty, reasoning about evidence is often straightforward; in practice, however, most conclusions from data involve uncertainty. In these situations,
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1 Richard Morey writes: Rink Hoekstra and I are undertaking some research to explore how people use classical statistical results to evaluate the weight of evidence. [sent-1, score-0.919]
2 Bayesians often critique classical techniques for being difficult to interpret in terms of what scientists want to know, but there is clearly information in the statistics themselves. [sent-2, score-0.692]
3 Below is our official announcement; it would be great if you could let people on your blog know about the survey, as we want to get a wide variety of statistical users to take the survey. [sent-4, score-0.356]
4 Announcement follows: Empirical science is grounded on the belief that data can be used as evidence. [sent-5, score-0.21]
5 The convincingness of data — the “weight” of the evidence they provide — is crucial to deciding between rival scientific positions. [sent-6, score-0.698]
6 In situations with no uncertainty, reasoning about evidence is often straightforward; in practice, however, most conclusions from data involve uncertainty. [sent-7, score-0.867]
7 In these situations, we obviously prefer strong evidence to weak evidence, but beyond this, strikingly little is known about how scientists actually evaluate the strength of evidence and to what extent scientists differ in their evaluations. [sent-8, score-1.82]
8 We are looking for researchers with experience using statistics to complete a short survey about the weight of evidence provided by statistics. [sent-9, score-1.113]
9 Participants are asked to assess the weight of evidence in several research scenarios. [sent-10, score-0.804]
10 The survey takes about 15 minutes to complete; if you would like to participate, click or copy/paste the link . [sent-11, score-0.405]
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same-blog 1 1.0000001 1681 andrew gelman stats-2013-01-19-Participate in a short survey about the weight of evidence provided by statistics
Introduction: Richard Morey writes: Rink Hoekstra and I are undertaking some research to explore how people use classical statistical results to evaluate the weight of evidence. Bayesians often critique classical techniques for being difficult to interpret in terms of what scientists want to know, but there is clearly information in the statistics themselves. We wonder how people extract that information. Below is our official announcement; it would be great if you could let people on your blog know about the survey, as we want to get a wide variety of statistical users to take the survey. Announcement follows: Empirical science is grounded on the belief that data can be used as evidence. The convincingness of data — the “weight” of the evidence they provide — is crucial to deciding between rival scientific positions. In situations with no uncertainty, reasoning about evidence is often straightforward; in practice, however, most conclusions from data involve uncertainty. In these situations,
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Introduction: This post is by Phil. A little over three years ago I wrote a post about exercise and weight loss in which I described losing a fair amount of weight due to (I believe) an exercise regime, with no effort to change my diet; this contradicted the prediction of studies that had recently been released. The comment thread on that post is quite interesting: a lot of people had had similar experiences — losing weight, or keeping it off, with an exercise program that includes very short periods of exercise at maximal intensity — while other people expressed some skepticism about my claims. Some commenters said that I risked injury; others said it was too early to judge anything because my weight loss might not last. The people who predicted injury were right: running the curve during a 200m sprint a month or two after that post, I strained my Achilles tendon. Nothing really serious, but it did keep me off the track for a couple of months, and rather than go back to sprinting I switched t
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Introduction: This announcement might be of interest to some of you. The application deadline is in just a few days: The National Center for Complementary and Alternative Medicine at the National Institutes of Health is seeking an additional experienced statistician to join our Office of Clinical and Regulatory Affairs team. www.usajobs.gov is accepting applications through April 22, 2011 for the general announcement and April 21 for status (typically current federal employee) candidates. To apply to this announcement or for more information, click on the links provided below or the USAJobs link provided above and search for NIH-NCCAM-DE-11-448747 ( external ) or NIH-NCCAM-MP-11-448766 ( internal ). You have to be a U.S. citizen for this one.
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Introduction: Rink Hoekstra writes: A couple of months ago, you were visiting the University of Groningen, and after the talk you gave there I spoke briefly with you about a study that I conducted with Richard Morey, Jeff Rouder and Eric-Jan Wagenmakers. In the study, we found that researchers’ knowledge of how to interpret a confidence interval (CI), was almost as limited as the knowledge of students who had had no inferential statistics course yet. Our manuscript was recently accepted for publication in Psychonomic Bulletin & Review , and it’s now available online (see e.g., here ). Maybe it’s interesting to discuss on your blog, especially since CIs are often promoted (for example in the new guidelines of Psychological Science ), but apparently researchers seem to have little idea how to interpret them. Given that the confidence percentage of a CI tells something about the procedure rather than about the data at hand, this might be understandable, but, according to us, it’s problematic neve
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Introduction: Someone writes: Suppose I have two groups of people, A and B, which differ on some characteristic of interest to me; and for each person I measure a single real-valued quantity X. I have a theory that group A has a higher mean value of X than group B. I test this theory by using a t-test. Am I entitled to use a *one-tailed* t-test? Or should I use a *two-tailed* one (thereby giving a p-value that is twice as large)? I know you will probably answer: Forget the t-test; you should use Bayesian methods instead. But what is the standard frequentist answer to this question? My reply: The quick answer here is that different people will do different things here. I would say the 2-tailed p-value is more standard but some people will insist on the one-tailed version, and it’s hard to make a big stand on this one, given all the other problems with p-values in practice: http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf http://www.stat.columbia.edu/~gelm
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Introduction: Richard Morey writes: Rink Hoekstra and I are undertaking some research to explore how people use classical statistical results to evaluate the weight of evidence. Bayesians often critique classical techniques for being difficult to interpret in terms of what scientists want to know, but there is clearly information in the statistics themselves. We wonder how people extract that information. Below is our official announcement; it would be great if you could let people on your blog know about the survey, as we want to get a wide variety of statistical users to take the survey. Announcement follows: Empirical science is grounded on the belief that data can be used as evidence. The convincingness of data — the “weight” of the evidence they provide — is crucial to deciding between rival scientific positions. In situations with no uncertainty, reasoning about evidence is often straightforward; in practice, however, most conclusions from data involve uncertainty. In these situations,
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Introduction: Richard Morey writes: Rink Hoekstra and I are undertaking some research to explore how people use classical statistical results to evaluate the weight of evidence. Bayesians often critique classical techniques for being difficult to interpret in terms of what scientists want to know, but there is clearly information in the statistics themselves. We wonder how people extract that information. Below is our official announcement; it would be great if you could let people on your blog know about the survey, as we want to get a wide variety of statistical users to take the survey. Announcement follows: Empirical science is grounded on the belief that data can be used as evidence. The convincingness of data — the “weight” of the evidence they provide — is crucial to deciding between rival scientific positions. In situations with no uncertainty, reasoning about evidence is often straightforward; in practice, however, most conclusions from data involve uncertainty. In these situations,
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