A statistical hypothesis test is a statistical inference process used to examine whether the information available supports a particular hypothesis.
Hypothesis testing helps us to make probabilistic statements regarding population parameters.
In the statistics literature, statistical hypothesis testing is critical. Two mathematically equivalent strategies are available.
The p-value is the probability that a certain finding (or more important result) would occur under the null hypothesis. At a significance level of 0.05, a fair coin would be projected to (incorrectly) reject the null hypothesis (that it is fair) in approximately 1 out of every 20 tests. The p-value does not indicate the likelihood that the null hypothesis or its opponent is correct.
There is an initial research hypothesis, the truth of which is unknown.
The first step is to state the relevant null and alternative hypotheses. This is crucial since poorly articulating the hypotheses will obscure the rest of the procedure.
The second stage is to examine the statistical assumptions made about the sample during the test, such as statistical independence or the shape of the observational distributions. This is also significant since incorrect assumptions lead to improper test findings.
Determine the appropriate test and state the relevant test statistic .
Derive the distribution of the test statistic under the null hypothesis from the assumptions. In most cases, this will be a foregone conclusion.
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