Question

Q 1. Imagine you compare the effectiveness of four different types of stimulant to keep you awake while revising statistics using a one-way ANOVA. The null hypothesis would be that all four treatments have the same effect on the meantime kept awake. How would you interpret the alternative hypothesis (only focus on the F-value [ANOVA] table here, not planned contrasts or posthocs)?

Q2. Independent ANOVA tests that the group means are a better fit of the data than what?

Q3. What does it mean when we refer to some post-hoc tests as being liberal and others as being conservative.

4. When would a researcher need to interpret the statistics in this table? Robust Tests of Equality of Means Wellbeing Statistic df df2 .943 Sig .054 026 Welch 4.320 Brown-Forsythe 5.119 2 11.574 a. Asymptotically F distributed Please refer to the table below to explain when we would interpret the contrasts in the Assume equal variances' rows and when we would interpret the contrasts in the 'Does not assume equal variances rowS 5. Contrast Tests Sig. (2- tailed) Value of Contrast Contras Std. Error df 3.80 1.80 3.80 1.80 12 029 065 031 086 Wellbeing Assume equal variances 1 1.536 2.474 8872.029 12 Does not assume equal variances .483 2.562 8.74 917 1.964 7.720

Answer 1

1)

At least two of the stimulants will have different effects on the mean time spent awake.

2)

Both the repeated-measures and independent ANOVA