When dealing with analysis and interpretation of data, it is very important to identify errors related to analysis to obtain clear conclusion.
The common errors related to Null Hypothesis are Type I and Type II errors.
Type I error or Alpha (A): This occurs when we are rejecting a true null hypothesis. It can be easily remembered as A -- R. A can refer to true null hypothesis.
Type II error or Beta (B): This occurs when we are accepting a wrong null hypothesis. It can be easily remembered as B -- A. B can refer to wrong null hypothesis.
As a step to reduce both errors simultaneously, the sample size should be increased to obtain clearer picture about the scenario. When the data tends to follow normal distribution clearly, the errors are reduced.
Another step is to increase the reliability or credibility of the data that is studied to prevent the occurrence of the errors.
From Beta, we can calculate the power of the test. That Power of test = 1 - B.
