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Statistical hypothesis testing
Adriana Albu,Loredana Ungureanu
Politehnica University Timisoara, adrianaa@aut.utt.ro
Politehnica University Timisoara, loredanau@aut.utt.ro
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-sp
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