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In contrast to its common definition and calculation, interpretation of p-values diverges among statisticians. Since p-value is the basis of various methodologies, this divergence has led to a variety of test methodologies and evaluations of test results. This chaotic situation has complicated the application of tests and decision processes. Here, the origin of the divergence is found in the prior probability of the test. Effects of difference in Pr(H0 = true) on the character of p-values are investigated by comparing real microarray data and its artificial imitations as subjects of Students t-tests. Also, the importance of the prior probability is discussed in terms of the applicability of Bayesian approaches. Suitable methodology is found in accordance with the prior probability and purpose of the test.
Bland and Altman plot method is a graphical plot approach that compares related data sets, supporting the eventual replacement of a measurement method for another one. Perhaps due to its graphical easy output it had been widely applied, however often
Any Bayesian analysis involves combining information represented through different model components, and when different sources of information are in conflict it is important to detect this. Here we consider checking for prior-data conflict in Bayesi
Rejoinder to Statistical Modeling of Spatial Extremes by A. C. Davison, S. A. Padoan and M. Ribatet [arXiv:1208.3378].
For testing two random vectors for independence, we consider testing whether the distance of one vector from a center point is independent from the distance of the other vector from a center point by a univariate test. In this paper we provide condit
In this paper, we study the problem of fair classification in the presence of prior probability shifts, where the training set distribution differs from the test set. This phenomenon can be observed in the yearly records of several real-world dataset