Question

What does the p-value mean?

A. The p-value is the probability of rejecting the null hypothesis if the test were repeated for different samples.
B. The p-value is the probability of obtaining a result at least as extreme as the one obtained with this sample given that the null hypothesis is true.
C. The p-value is the probability of falsely rejecting the null hypothesis.
D. The p-value is the probability that the null hypothesis is true.
c. What assumption about the population distribution of the two banks is necessary in (a)? Is the assumption valid for these data? What assumption about the population distribution of the two banks is necessary in (a)?
A. The sample sizes taken from the population are independent and identically distributed.
B. The populations do not contain any extreme outliers.
C. Each of the two populations must be normally distributed.
D. There is enough information given that no assumptions need to be made.
Is the assumption valid for these data?
A. The assumption is not valid because histograms of the data do not appear to be bell shaped, which is a good indication of possible non-normality.
B. The assumption is valid because histograms of the data appear to be bell shaped, which is a good indication of possible normality.
C. The assumption is not valid because both populations contain outliers.
D. The assumption is not valid because the sample sizes are not independent.
d. Assume the results of part (a) are valid. Based on the results of (a), is it appropriate to use the pooled-variance t test to compare the means of the two be
A. No, because the population variances cannot be assumed to be equal. a. Is there evidence of a difference in the variability of the waiting time between the two banks?

Answer 1

The p-value is a measure of the strength of evidence against the null hypothesis in a statistical hypothesis test.

Step-by-step explanation:

The p-value measures the strength of evidence against the null hypothesis in a statistical hypothesis test. It is the probability of obtaining a result at least as extreme as the one observed, assuming that the null hypothesis is true.

A smaller p-value indicates stronger evidence against the null hypothesis and suggests that the alternative hypothesis may be true. It is commonly used to determine whether to reject or fail to reject the null hypothesis in favor of the alternative hypothesis.