“Little Data” etc.: My talk at NYU this Friday, 8 Dec 2017

December 5, 2017
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(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

I’ll be talking at the NYU business school, in the department of information, operations, and management sciences, this Fri, 8 Dec 2017, at 12:30, in room KMC 4-90 (wherever that is):

Little Data: How Traditional Statistical Ideas Remain Relevant in a Big-Data World; or, The Statistical Crisis in Science; or, Open Problems in Bayesian Data Analysis

“Big Data” is more than a slogan; it is our modern world in which we learn by combining information from diverse sources of varying quality. But traditional statistical questions—how to generalize from sample to population, how to compare groups that differ, and whether a given data pattern can be explained by noise—continue to arise. Often a big-data study will be summarized by a little p-value. Recent developments in the social and medical sciences have made it clear that our usual statistical prescriptions, adapted as they were to a simpler world of agricultural experiments and random-sample surveys, fail badly and repeatedly in the modern world in which millions of research papers are published each year. Can Bayesian inference help us out of this mess? Maybe, but much research will be needed to get to that point.

The post “Little Data” etc.: My talk at NYU this Friday, 8 Dec 2017 appeared first on Statistical Modeling, Causal Inference, and Social Science.



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