Answer:
To calculate the P-value of a t-test, we need to consider the given information for each situation.
(a) In the first situation, we have an upper-tailed test with a degree of freedom (df) of 9 and a t-value of 2.00. The P-value represents the probability of obtaining a t-value as extreme or more extreme than the observed t-value under the null hypothesis.
To determine the P-value, we can use statistical software or tables. Since you mentioned a button hyperlink to the SALT program (which I assume provides the necessary calculations), it is recommended to utilize that resource. By clicking on the "Use SALT" button, you can access the program and input the required values. SALT will then calculate the P-value for you in this specific situation, rounding it to four decimal places.
(b) In the second situation, we also have an upper-tailed test, but this time with a sample size (n) of 13 and a t-value of 3.30. Similar to the first situation, we need to determine the P-value to assess the significance of our results.
Again, it is recommended to use the provided SALT program by clicking on the corresponding button hyperlink. Enter the given values of n = 13 and t = 3.30 into SALT, and it will calculate the P-value for you, rounding it to four decimal places.
Remember, the P-value represents the probability of obtaining a t-value as extreme or more extreme than the observed t-value under the null hypothesis. By comparing the calculated P-value to a predefined significance level (usually denoted by α), such as 0.05, we can assess the statistical significance of our test results. If the P-value is less than the significance level, we reject the null hypothesis; otherwise, we fail to reject it.