The emergence of large data sets in environmental science has changed statistical analysis: more focus on data wrangling and algorithmic approaches, especially Bayes. Bayesian analysis departs from alternative methods in its application of probability to all aspects of model fitting with side benefits of simplifying interpretation. The elements of a Bayesian analysis include distributions for prior, likelihood, and posterior. Hierarchical models emerge naturally in the Bayesian framework as a means for analyzing high-dimensional problems without requiring a change in approach. Graphs help to organize hierarchical modeling. Basic concepts are introduced using regression.
Discussion reading: select two papers and come prepared to discuss them. Post questions for discussion on sakai resources/groupDocs.
Reproducibility (in The Atlantic and NY Times) is intimately linked to the notion of uncertainty and, thus, probability. Statistics are used both to communicate uncertainty and for forensic analyses of questionable results.
Redefining statistical significance, motivated in part by the reproducibility crisis in psychology, classical and Bayesian statisticians compromise; if \(P\) values are to used, then \(P = 0.05\) is way too high, Nature.
Why Big Data Could Be a Big Fail, Jordan on potential and limitations of Big Data (misleading title).
Why environmental scientists are becoming Bayesians, why the proliferation of Bayes in environmental science, Ecol Letters.
post problems in Intro to R to Sakai, one set per group
Recall objectives: