A Fervent defense of Frequentist Statistics. Eliezer Yudkowsky's comments are also well worth reading. Most notably:

Who are these mysterious straw Bayesians who refuse to use algorithms that work well and could easily turn out to have a good explanation later? Bayes is epistemological background not a toolbox of algorithms.

After thinking about the online learning example discussed in the above post, I came to the realization that Eliezer Yudkowsky came to a long time back. Randomization (aka "non-bayesian") algorithms are effective in adversarial problems not because Bayesian reasonining fails, but because randomization reduces the advantage that your adversaries intelligence provides. The adversarial bandit is fundamentally not a statistics problem at all.

Scala's Types of Types. Great article explaining Scala's type system.

Peter Thiel is wrong about the minimum wage. This example just goes to show why a concrete model is so important - Peter Thiel is a very smart guy, yet his verbal reasoning is easily debunked by a very simple graph.

Female Risk Aversion

Hip Gadgets For The Developing World Won't Solve Global Poverty. See also this article.

Republicans understand evolution better than democrats. They don't agree with it, but they understand it. Republicans are also more likely to know that the Earth revolves around the Sun once every year. Stereotype busted, I guess.

Duality and Coclustering

DO NOT USE CONFIDENCE INTERVALS. See the paper Robust Misinterpretation of Confidence Intervals. They are a useless tool for communicating with non-statisticians - anyone without a PhD will interpret your confidence interval as a credible interval. I've all observed this in practice, but it's good to have stats to back it up.

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