1. Innovative Teaching Article (slides with auxiliary information: © 2014) James W. Grice Oklahoma State University Department of Psychology

2. Variable Models and Population Parameters Optimism Delay in Evaluation r = -0.18* Assumption-laden NHST Assumptions Linear relationship in population Continuous variables Random sampling Bivariate Normal population distribution Homoscedasticity Independence of pairs of observations H o is true “p ≤.05” is proper significance level Goal is to estimate a population parameter; here, r pop, the population correlation

4. Variable Models and Population Parameters Therapy vs. Control Weight Gain Hypotheses:H o : μ Therapy = μ Control H A : μ Therapy > μ Control  predicted or μ Therapy < μ Control M Therapy = +1.35 (SD = 3.72) pounds, M Control = -1.23 (SD = 3.25) pounds, t(58) = 2.86, p =.006, d =.74 (large effect using Cohen’s conventions), 95% CI: 0.77 ≤ μ Therapy - μ Control ≤ 4.38) Goal is to estimate a population parameter; here, μ Therapy - μ Control = μ Diff, the difference between population means.

5. Assumptions for Independent t-test and p-value: Random sampling or random assignment to groups Continuous dependent variable Normal population distributions Observations are independent both within and between groups Homogeneity of population variances Null hypothesis is true p ≤.05 is a reasonable cut-point for statistical significance Variable Models and Population Parameters

6. Continuities and Variables to Entities and Qualities

7. The End