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From |
George Murray <george.murray16@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Computing differences in probability of type-one error for different samples |

Date |
Thu, 17 May 2012 03:27:29 +1000 |

Statalisters, Suppose I run a certain model ~1000 times, but a different sample is used each type the model is run. The statistical significance of one of the variables is tested for *all* of the ~1000 models. I am aware that the (of course, arbitrarily chosen) significance level can be used to find the probability that a type I error has been made but obviously, the probability of type-I error will not be uniform across each of the models where the null is rejected. Is it then possible to use Stata to compute the probability of that a type I error has been made (or is the only solution Bayesian techniques?) Is it blasphemous to use the p-value (such that the differing probabilities of a type-I error is adjusted for) as an approximation, given that I have no a priori information? Is anyone aware of any applied papers which have (rightly or wrongly) used this to approximate that a type-I error has been made? Since the same model is used, is it possible that the p-value is inversely related to the probability of a type-I error. (Apologies if this question sounds really silly). Regards, George. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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