The image illustrates a right tail test on a normal distribution curve where the null hypothesis is not rejected. Below the graph are terms that need to be matched to specific points on the curve.### Graph Explanation:- **Normal Distribution Curve**: The blue curve represents the normal distribution.- **Null Hypothesis Value (a)**: Labeled as "a", this point is at the center of the curve, representing the mean under the null hypothesis.- **P-value (b)**: Labeled as "b", this point is further to the right on the horizontal axis within the shaded area, representing the P-value region.- **Significance Level (c)**: Labeled as "c", this point, marked in red, lies very close to "d", within the shaded tail area.- **Test Statistic (d)**: Labeled as "d", this is the point marking the boundary of the test statistic's distribution, located just at the edge of the shaded region.- **Critical Value (e)**: Labeled as "e", it is positioned at the beginning of the shaded section marking the boundary for rejection.### Label Explanation:- **Significance Level**: Represents the threshold at which the test determines statistical significance; located at point "c".- **Null Hypothesis Value**: The hypothesized average value, shown at point "a".- **Test Statistic**: The computed value from the sample data, labeled as "d".- **P-value**: Probability of observing a test statistic as extreme as, or more extreme than, the one observed; marked as "b".- **Critical Value**: The threshold beyond which the null hypothesis is rejected; located at "e".This setup visually explains the concepts involved in hypothesis testing on a normal distribution, showing where and why the null hypothesis remains accepted due to the arrangement of the values.

Given: The drawing represents a right-tailed test where we fail to reject the null hypothesis. The objective is to label each letter in the drawing using the given choices. The level of significance is strength of evidence that need to be present in a sample before the rejection of the null hypothesis. In other words, it is the probability of wrongly rejecting the null hypothesis when the null hypothesis is actually true. It is known as the type I error. P-value is the probability of obtaining the value of the test statistic or a value even more extreme, assuming the null hypothesis is true. And the decision rule is: Reject the null hypothesis, if the p-value is less than or equal to significance level OR the test statistic is greater than critical value at given significance level. Do not reject the null hypothesis, if the p-value is greater than significance level OR the test statistic is less than critical value at given significance level. In the given case, we fail to reject the null hypothesis. This means, the p-value is greater than the significance level. And the value of the test statistic is less than the critical value. Therefore, the distribution of normal curve depicting a right-tailed test is given below: Answers: Significance level - b Null Hypothesis value - a Test statistic - d Critical value - c P-value - e