![]() ![]() That make me more sense that an real randomness in nature. I think that with new research in more general and computational intensive frequentist inference methods many of the problems of this aproach can be resolve at least in part.To be fair I like some points of Bayesian statistics, specially the fact the probabilities are not related with inherent randomness but the ignorance of causes of phenomena. The Bayesian statistics can not be properly compute the in the complicate cases, and only with the arising of tractable numerical aproximations is that the Bayesian methods become competitive. and compare the observed value with a table. The lack of numerical power only make possible use simple statistics as mean, variance, kurtosis, etc. Also many difficulties in freq stats arise from the fact their method where develop at early 20 century. A degree of belief is difficult to interpret and to compare with experiment. Also the freq def of probability is more intuitive and provide a clear meaning to statement p=0.68. The difficulty of model properly the prior distribution have as result additional discussion over this step of inference and not over the results of the inference. The freq stats are the most widely used because it make difficult problems and models tractable using scalar scstiatits, and made direct inferences that although relies strongly in asymptotic distribution provide an inference which everybody agrees in the result. ![]() I think the frequentist statistics have the advantages and disadvantages, the same as Bayesian stats. ![]()
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