In statistics, type I error is defined as an error that occurs when the sample results cause the rejection of the null hypothesis, in spite of the fact that it is true. It is incorrect acceptance of false null hypothesis. It is incorrect rejection of true null hypothesis. Type II error is the acceptance of hypothesis which ought to be rejected. Type I error refers to non-acceptance of hypothesis which ought to be accepted.
There are slight and subtle differences between type I and type II errors, that we are going to discuss in this article. An alternative hypothesis (H 1) is a premise that expects some difference or effect. The null hypothesis is a proposition that does not expect any difference or effect. The result of testing is a cornerstone for accepting or rejecting the null hypothesis (H 0). The testing of hypothesis is a common procedure that researcher use to prove the validity, that determines whether a specific hypothesis is correct or not.
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