概率论毕业论文外文翻译--统计假设检验（可编辑）.doc

概率论毕业论文外文翻译--统计假设检验 Statistical hypothesis testing Adriana Albu,Loredana Ungureanu Politehnica University Timisoara, adrianaa@//0. Politehnica University Timisoara, loredanau@//. Abstract In this article, we present a Bayesian statistical hypothesis testing inspection, testing theory and the process Mentioned hypothesis testing in the real world and the importance of, and successful test of the Notes. Key words Bayesian hypothesis testing; Bayesian inference; Test of significance Introduction A statistical hypothesis test is a method of making decisions using data, whether from a controlled experiment or an observational study not controlled. In statistics, a result is called statistically significant if it is unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level. The phrase test of significance was coined by Ronald Fisher: Critical tests of this kind may be called tests of significance, and when such tests are available we may discover whether a second sample is or is not significantly different from the first.[1] Hypothesis testing is sometimes called confirmatory data analysis, in contrast to exploratory data analysis. In frequency probability, these decisions are almost always made using null-hypothesis tests. These are tests that answer the question Assuming that the null hypothesis is true, what is the probability of observing a value for the test statistic that is at least as extreme as the value that was actually observed? 2[] More formally, they represent answers to the question, posed before undertaking an experiment, of what outcomes of the experiment would lead to rejection of the null hypothesis for a pre-specified probability of an incorrect rejection. One use of hypothesis testing is deciding whether experimental results contain enough information to cast doubt on conventional wisdom. Statistical hypothesis testing is a k