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Hypothesis is making an assumption. In Statistics a Hypothesis or an assumption is taken first and then the Hypothesis is tested as how accurate it is or not. Hypothesis testing is a study based on statistical accuracy of an experiment. If the result is positive i.e. if the assumption is correct or approximate, then it is called Statistically Significant.
There are two types of statistical hypotheses
Reject the null hypothesis and accept the alternative hypothesis or Fail to reject the null hypothesis and state that there is not enough evidence to support the alternative hypothesis.
Statisticians follow a formal process to determine whether to accept or reject null-hypothesis, based on sample data. This process is called hypothesis testing and consists of four steps.
Decision Errors - Two types of errors can result from a hypothesis test
Decision Rules - The analysis plan includes decision rules for rejecting the null-hypothesis. In practice, statisticians describe these decision rules in two-ways with reference to a P-value or with reference to a region of acceptance.
The set of values outside the region of acceptance is called the region of rejection. If the test statistic falls under the region of rejection, the null-hypothesis is rejected. In such cases, we say that the hypothesis has been rejected at the α level of significance.
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