in Machine Learning

Practical advice about research modelling with Andrew

A post about ROC analysis becomes a small lecture about decision analysis:

It’s good for researchers to present their raw data, along with clean summary analyses. Report what your data show, and publish everything! But when it comes to decision making, including the decision of what lines of research to pursue further, I’d go Bayesian, incorporating prior information and making the sources and reasoning underlying that prior information clear, and laying out costs and benefits. Of course, that’s all a lot of work, and I don’t usually do it myself. Look at my applied papers and you’ll see tons of point estimates and uncertainty intervals, and only a few formal decision analyses. Still, I think it makes sense to think of Bayesian decision analysis as the ideal form and to interpret inferential summaries in light of these goals. Or, even more, short-term than that, if people are using statistical significance to make publication decisions, we can do our best to correct for the resulting biases, as in section 2.1 of this paper.

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