For this week's post, think of two variables you wish to compare using ANOVA (Analysis of variance). ...

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For this week's post, think of two variables you wish to compare using ANOVA (Analysis of variance). Recall that ANOVA is a comparison of means test for categorical IVs (nominal or ordinal with 3 or more categories) and interval/ratio DVs. Write a post (150 words as a guideline) in which you discuss your IV and DV, theory for why they may be related, and what you expect from an ANOVA test in the difference in means (for instance, which categories of IV will have a higher value on DV compared to others, etc.). Then, explain why the model level statistics for the ANOVA test (the F test) is insufficient for determining specific relationships between categories of the IV and why you’d need additional, ‘post-hoc’ tests to determine where your sub-sample means may be statistically different (i.e. Tukey’s HSD). You can also conclude your post by briefly discussing the interpretation of Tukey's HSD test. Good luck! *Although this is Week 12 post, the content corresponds to the Week 11 lecture/lab content. week 11 post: Let us explore whether exercise impacts sleep duration (DV - interval data, measured in minutes). We argue that sleep pattern is improved by regular exercise (IV - dichotomous, exercise versus no exercise) through a reduction in stress and physical fatigue. We expect to reject the null hypothesis, meaning an average sleep duration difference exists between the exercise and non-exercise groups. We anticipate the exercise group will have a higher sleep duration due to the theorized benefits. Independent Samples t-test: Sleep Duration by Exercise Group, t(df=38) = 2.34, p = 0.024 The T-test statistic (2.34) appears significant enough to differentiate sleep duration between exercise and non-exercise groups. Observing the p-value (0.024) being statistically significant, the null hypothesis can be rejected. That supports our expectation: individuals who exercise regularly tend to sleep longer on average.