Answer:
1) Option C is correct.
Difference should be 0.
2) Option D is correct.
The cutoff t-scores are -2.262, 2.262.
t < -2.262, t > 2.262
3) Option A is correct.
The variance = (135/10) = 13.5
Explanation:
1) For any hypothesis test, the first step is usually to define the null and alternative hypothesis.
In hypothesis testing, especially one comparing two sets of data, the null hypothesis plays the devil's advocate and usually takes the form of the opposite of the theory to be tested. It usually contains the signs =, ≤ and ≥ depending on the directions of the test. It usually maintains that, with random chance responsible for the outcome or results of any experimental study/hypothesis testing, its statement is true.
The alternative hypothesis usually confirms the theory being tested by the experimental setup. It usually contains the signs ≠, < and > depending on the directions of the test. It usually maintains that significant factors other than random chance, affect the outcome or results of the experimental study/hypothesis testing and result in its own statement.
For this test, the counselling psychologist wants to check if there is a difference, whether positive or negative between the procrastination times before and after attending the workshop.
So, the appropriate parameter to compute would be the difference in procrastination times before and after the workshop.
Then the null hypothesis would be that the difference between these two times is 0.
Alternative hypothesis would be that there is no difference between the two times.
Mathematically, if the difference between the two times is p.
The null hypothesis is represented as
H₀: p = 0
The alternative hypothesis is represented as
Hₐ: p ≠ 0
Hence, it is evident that the comparison population would be the difference scores being 0.
b) This test aims to test whether there's a negative or positive difference in the scores before and after the workshop.
So, the test will be in both directions. The cut off t-score indicating the rejection regions would be found using the significance level provided (0.05) and the degree of freedom.
degree of freedom = df = n - 1 = 10 - 1 = 9
t(9, 0.05) = ± 2.2622 (from the t-distribution tables)
c) Variance is an average of the squared deviations from the mean.
Mathematically, estimate of the population variance is given as
Variance = σ² = [Σ(x - xbar)²/N]
Where Σ(x - xbar)² = square of the deviations from the mean = 135
N = Sample Size = 135
So, variance = (135/10) = 13.5
Hope this Helps!!!