andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2343 knowledge-graph by maker-knowledge-mining
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Introduction: Gary Marcus and Ernest Davis wrote this useful news article on the promise and limitations of “big data.” And let me add this related point: Big data are typically not random samples, hence the need for “big model” to map from sample to population. Here’s an example (with Wei Wang, David Rothschild, and Sharad Goel): Election forecasts have traditionally been based on representative polls, in which randomly sampled individuals are asked for whom they intend to vote. While representative polling has historically proven to be quite effective, it comes at considerable financial and time costs. Moreover, as response rates have declined over the past several decades, the statistical ben- efits of representative sampling have diminished. In this paper, we show that with proper statistical adjustment, non-representative polls can be used to generate accurate election forecasts, and often faster and at less expense than traditional survey methods. We demon- strate this approach
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1 Gary Marcus and Ernest Davis wrote this useful news article on the promise and limitations of “big data. [sent-1, score-0.274]
2 Here’s an example (with Wei Wang, David Rothschild, and Sharad Goel): Election forecasts have traditionally been based on representative polls, in which randomly sampled individuals are asked for whom they intend to vote. [sent-3, score-0.886]
3 While representative polling has historically proven to be quite effective, it comes at considerable financial and time costs. [sent-4, score-0.673]
4 Moreover, as response rates have declined over the past several decades, the statistical ben- efits of representative sampling have diminished. [sent-5, score-0.319]
5 In this paper, we show that with proper statistical adjustment, non-representative polls can be used to generate accurate election forecasts, and often faster and at less expense than traditional survey methods. [sent-6, score-1.119]
6 We demon- strate this approach by creating forecasts from a novel and highly non-representative survey dataset: a series of daily voter intention polls for the 2012 presidential election conducted on the Xbox gaming platform. [sent-7, score-1.636]
7 After adjusting the Xbox responses via multilevel regression and poststratification, we obtain estimates in line with forecasts from leading poll analysts, which were based on aggregating hundreds of traditional polls conducted during the election cycle. [sent-8, score-1.445]
8 We conclude by arguing that non-representative polling shows promise not only for election forecasting, but also for measuring public opinion on a broad range of social, economic and cultural issues. [sent-9, score-0.872]
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Introduction: I’ve written a lot on polls and elections (“a poll is a snapshot, not a forecast,” etc., or see here for a more technical paper with Kari Lock) but had a few things to add in light of Sam Wang’s recent efforts . As a biologist with a physics degree, Wang brings an outsider’s perspective to political forecasting, which can be a good thing. (I’m a bit of an outsider to political science myself, as is my sometime collaborator Nate Silver, who’s done a lot of good work in the past few years.) But there are two places where Wang misses the point, I think. He refers to his method as a “transparent, low-assumption calculation” and compares it favorably to “fancy modeling” and “assumption-laden models.” Assumptions are a bad thing, right? Well, no, I don’t think so. Bad assumptions are a bad thing. Good assumptions are just fine. Similarly for fancy modeling. I don’t see why a model should get credit for not including a factor that might be important. Let me clarify. I
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