a) if the null hypothesis is true, you'll get a high p-value. b) if the null hypothesis is true, a p-value of 0.01 will occur about 1% of the time. c) a p-value of 0.90 means that the null hypothesis has a good chance of being true. d) a p-value of 0.90 is strong evidence that the null hypothesis is true.

a) If the null hypothesis is true, you'll get a high P-value. (it depends)

b) If the null hypothesis is true, a P-value of 0.01 will occur about 1% of the time. (false)

c) A P-value of 0.90 means that the null hypothesis has a good chance of being true. (not only has a good chance it has strong evidence)

d) A P-value of 0.90 is strong evidence that the null hypothesis is true.(true)

Step-by-step explanation:

Before i answer this question, you need to understand that p-values give you the clues to identify when you can accept the null hypothesis ( null hypothesis is true) and when you can reject the null hypothesis (null hypothesis is not true).

1. When you get a small p-value  (typically ≤ 0.05) values that are less or equal to 0.05, for example 0.01,  you reject the null hypothesis (null hypothesis is not true)

2. when you get a large p-value (> 0.05) values that are greater than 0.05, for example 0.94, 0.90. you can accept the null hypothesis because  indicates weak evidence against the null hypothesis (null hypothesis is true).

This is the explanation:

a) if the null hypothesis is true  you`ll get a high p-value only if the p-value is ≥ 0.05

b) if p value is less or equal to 0.05. Null hypothesis is not true.

c) A P-value of 0.90 means that the null hypothesis has a good chance of being true . It not only has a good chance it is strong evidence that null hypothesis is true.

d) A p-value of 0.90 is strong evidence that null hypothesis is true. p-values that are greater than 0.05  you can accept the null hypothesis (null hypothesis is true).