andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1950 knowledge-graph by maker-knowledge-mining
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Introduction: Statistical Methods and Data Skepticism Data analysis today is dominated by three paradigms: null hypothesis significance testing, Bayesian inference, and exploratory data analysis. There is concern that all these methods lead to overconfidence on the part of researchers and the general public, and this concern has led to the new “data skepticism” movement. But the history of statistics is already in some sense a history of data skepticism. Concepts of bias, variance, sampling and measurement error, least-squares regression, and statistical significance can all be viewed as formalizations of data skepticism. All these methods address the concern that patterns in observed data might not generalize to the population of interest. We discuss the challenge of attaining data skepticism while avoiding data nihilism, and consider some proposed future directions. Stan Stan (mc-stan.org) is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a
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1 Statistical Methods and Data Skepticism Data analysis today is dominated by three paradigms: null hypothesis significance testing, Bayesian inference, and exploratory data analysis. [sent-1, score-0.627]
2 There is concern that all these methods lead to overconfidence on the part of researchers and the general public, and this concern has led to the new “data skepticism” movement. [sent-2, score-0.806]
3 But the history of statistics is already in some sense a history of data skepticism. [sent-3, score-0.477]
4 Concepts of bias, variance, sampling and measurement error, least-squares regression, and statistical significance can all be viewed as formalizations of data skepticism. [sent-4, score-0.651]
5 All these methods address the concern that patterns in observed data might not generalize to the population of interest. [sent-5, score-0.741]
6 We discuss the challenge of attaining data skepticism while avoiding data nihilism, and consider some proposed future directions. [sent-6, score-1.053]
7 org) is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. [sent-8, score-0.448]
8 We are also developing Stan as a more general statistical modeling and computing platform that will be able to do optimization, variational inference, and expectation propagation, as well as full Bayes. [sent-9, score-0.762]
9 Unfortunately something came up and I won’t be able to do either of those talks. [sent-11, score-0.106]
10 An old version of the Stan talk is here but I was planning to present some new material too. [sent-14, score-0.084]
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Introduction: Statistical Methods and Data Skepticism Data analysis today is dominated by three paradigms: null hypothesis significance testing, Bayesian inference, and exploratory data analysis. There is concern that all these methods lead to overconfidence on the part of researchers and the general public, and this concern has led to the new “data skepticism” movement. But the history of statistics is already in some sense a history of data skepticism. Concepts of bias, variance, sampling and measurement error, least-squares regression, and statistical significance can all be viewed as formalizations of data skepticism. All these methods address the concern that patterns in observed data might not generalize to the population of interest. We discuss the challenge of attaining data skepticism while avoiding data nihilism, and consider some proposed future directions. Stan Stan (mc-stan.org) is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a
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