A researcher wants to investigate claims that a new plant compound is effective in curbing appetite. She randomly assigns 26 rats to a treatment or control group (13 rats in each group). Rats in the treatment group are given the plant compound daily for a week, while rats in the control group are given a placebo. Over the course of the week, the researcher monitors the total number of calories consumed by all 26 rats. All the rats are kept separate from one another, but in their typical environments, for the duration of the week.
The researcher’s null hypothesis is that the rats given the plant compound consume the same number of calories as the rats given a placebo. Since the rats in the treatment group are independent of the rats in the control group, the researcher realizes that the design of her study is an independent-measures design. The researcher knows that the total calories consumed by a rat over the course of a week are normally distributed. However, the researcher is not sure that the standard deviation (σ) of the total calories consumed by a rat over the course of a week is the same regardless of whether the rat is given the plant compound or a placebo.
Suppose the mean number of weekly calories consumed by the rats given the plant compound (the treatment group) is 775 with a standard deviation of 41, and the mean number of calories consumed by the rats in the control group is 984 with a standard deviation of 75.
The researcher is unsure of which of the following required assumptions for the independent-measures t test?

Are the observations within each group independent?
Are the control and treatment groups independent of one another?
Is there homogeneity of variance?
Are the calories consumed by the rats in each group normally distributed?
The researcher decides to use a Hartley’s F-max test. The value of F-max is .
Using the partial Critical Values for the F-max Statistic table below, the critical value for the researcher’s F-max with α = 0.01 is .
Critical Values for the F-max Statistic (Partial)
Critical values for α = .05 are in regular type; critical values for α = .01 are bold.
k = Number of Samples
n – 1 2 3
4 9.60 15.5
23.2 37.0
5 7.15 10.8
14.9 22.0
6 5.82 8.38
11.1 15.5
7 4.99 6.94
8.89 12.1
8 4.43 6.00
7.50 9.9
9 4.03 5.34
6.54 8.5
10 3.72 4.85
5.85 7.4
12 3.28 4.16
4.91 6.1
15 2.86 3.54
4.07 4.9
20 2.46 2.95
3.32 3.8
30 2.07 2.40
2.63 3.0
60 1.67 1.85
1.96 2.2
Given the results of the F-max test, the researcher’s conclusion is that .
Can you still use the independent-measures t test if the homogeneity of variance assumption is violated?

Yes, you don’t use a pooled variance and you recompute the degrees of freedom so that the resulting df is smaller.
Yes, you don’t use a pooled variance and you recompute the degrees of freedom so that the resulting df is larger.
No, you cannot use the independent-measures t test.