Question

Which of the following statements is true about the P-value?

A. It is the probability of committing type I error and it is the smallest significant level at which a null hypothesis would be rejected

B. It is the probability of committing type I error and it is the largest significant level at which a null hypothesis would be rejected

C. It is the probability of committing type II error and it is the smallest significant level at which a null hypothesis would be rejected

D.It is the probability of committing type II error and it is the largest significant level at which a null hypothesis would be rejected

Answer 1

The p-value or probability value is defined for a given statistical model, as the probability that, when the null hypothesis is true, the statistical summary will be equal to, or more extreme than, the actual observed result in the hypothesis testing.

When we use a hypothesis test in statistics, a p-value helps us to determine the significance of our results.

Hypothesis tests are used to test the validity of a claim that is said about a population. This claim is called null hypothesis.

The alternative hypothesis is one which we believe if the null hypothesis is concluded to be false.

So it is the probability of acceptance the null hypothesis. Higher the value of p value more significant the model.

Type I error is defined as the rejection of a true null hypothesis.

Type II error is defined as the acceptance of a false null hypothesis.

It means a true statement about the P value is It is the probability of committing type I error and it is the smallest significant level at which a null hypothesis would be rejected.

Hence option A is the correct answer