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  title: '"I'm going to take the role of the "undergrad" h..."',
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  anchorContext: 'Bayesian:  I wrote pretty much the same Python program when I was first converting to Bayesianism and finding out about likelihood ratios and feeling skeptical about the system maybe being abusable in some way, and then a friend of mine found out about likelihood ratios and he wrote essentially the same program, also in Python\\.  And lo, he found that false evidence of 20:1 for the coin being 55% biased was found at least once, somewhere along the way\\.\\.\\. 1\\.4% of the time\\.  If you asked for more extreme likelihood ratios, the chances of finding them dropped off even faster\\.',
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  text: 'I'm going to take the role of the "undergrad" here and try to interpret this in the following way:\n\nGiven that a hypothesis is true -- but it is unknown to be true -- it is far more likely to come by a "statistically significant" result indicating it is wrong, than it is likely to come by a result indicating that another hypothesis is significantly more likely.\n\nIn simpler words - it is far easier to "prove" a true hypothesis is wrong by accident, than it is to "prove" that an alternative hypothesis is superior (a better estimator of reality) by accident.\n\nWould you consider this interpretation accurate? ',
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