> This post really has nothing to do with techniques or "bad data" or a bayesian vs. frequentist debate. I disagree, but maybe I'm missing something. This earl

> This post really has nothing to do with techniques or "bad data" or a bayesian vs. frequentist debate. I disagree, but maybe I'm missing something. This early quote, regarding 'bayesian vs. frequentist' seems to sum up O'Neil's view of Silver fairly well to me: "What is not reasonable, however, is for Silver to claim to understand how the financial crisis was a result of a few inaccurate models, and how medical research need only switch from being frequentist to being Bayesian to become more accurate." And later on regarding 'bad data,' a point she reiterates several times: "In other words, it’s not that there are bad statistical approaches which lead to vastly over-reported statistically significant results and published papers (which could just as easily happen if the researchers were employing Bayesian techniques, by the way). It’s that there’s massive incentive to claim statistically significant findings, and not much push-back when that’s done erroneously, so the field never self-examines and improves their methodology. The bad models are a consequence of misaligned incentives." I do think in the above quote O'Neil is equivocating between the idea of a 'bad model' and the skewed data or overreaching significance applied to the model. Her fundamental point, which she could have done a better job communicating I think, is that it wasn't the models themselves that were bad, bayesian or otherwise, but the data in the models and at times the inordinate significance applied to those models that was the real cause of the meltdown.