Significance of P-value and Alpha in Hypothesis testing.

[vishal_prof_gmail_com]

In a Hypthesis testing why do we compare p value with Alpha. As per definition Alpha is probability of rejecting null hypothesis when it is true , how is this related to p value?

[art297]

A fairly simple, but nice (I think) explanation of the relationship between the two can be found at:

What Is the Difference between Alpha and P-Values

[vishal_prof_gmail_com]

I have gone through this articles. It says we compare p-value with alpha , but the reason why alpha is chosen as the threshold value is not explained.

[art297]

I'm a Psychologist, not a statistician, thus I'll leave it to the statisticians to either verify or critique the statements.  However, that said, the following statement from the article I linked said: "The alpha value gives us the probability of a type I error. Type I errors occur when we reject a null hypothesis that is actually true. Thus, in the long run, for a test with level of significance of 0.05 = 1/20, a true null hypothesis will be rejected one out of every 20 times."

Why 0.05?  The article goes on to state: "Although in theory and practice many numbers can be used for alpha, the most commonly used is 0.05. The reason for this both because consensus shows that this level is appropriate, and historically it has been accepted as the standard."