(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
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 (mc-stan.org) is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. 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. We discuss how Stan works and what it can do, the problems that motivated us to write Stan, current challenges, and areas of planned development, including tools for improved generality and usability, more efficient sampling algorithms, and fuller integration of model building, model checking, and model understanding in Bayesian data analysis.
Unfortunately something came up and I won’t be able to do either of those talks. Bummer. I was looking forward to both. An old version of the Stan talk is here but I was planning to present some new material too.
The post My talks that were scheduled for Tues at the Data Skeptics meetup and Wed at the Open Statistical Programming meetup appeared first on Statistical Modeling, Causal Inference, and Social Science.
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