Two types of errors are defined for the p-value; these errors are given below:
It is defined as the incorrect or false rejection of the Null hypothesis. For this error, the maximum probability is alpha, and it is set in advance. The error is not affected by the sample size of the dataset. The type I error increases as we increase the number of tests or endpoints.
Type II error is defined as the wrong acceptance of the Null hypothesis. The probability of type II error is beta, and the beta depends upon the sample size and value of alpha. The beta cannot be determined as the function of the true population effect. The value of beta is inversely proportional to the sample size, and it means beta decreases as the sample size increases.
The value of beta also decreases when we increase the number of tests or endpoints.
We can understand the relationship between hypothesis testing and decision on the basis of the below table:
The importance of p-value can be understood in two aspects:
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