Step 1 :1. Null Hypothesis Test Step 1: State the hypotheses. The null hypothesis is that there is no difference in the mean number of "um" and "uh" used by the two groups. The alternative hypothesis is that there is a difference. H0: μ1 = μ2 H1: μ1 ≠ μ2 Step 2: Set the criteria for a decision. If the p-value is less than the significance level (0.05), we reject the null hypothesis. Step 3: Compute the test statistic. We need to calculate the pooled variance, the standard error, and finally the t-value. Pooled variance (s^2) = (SS1 + SS2) / (n1 + n2 - 2) = (88 + 112) / (4 + 6 - 2) = 200 / 8 = 25 Standard error (SE) = sqrt [ s^2 * (1/n1 + 1/n2) ] = sqrt [ 25 * (1/4 + 1/6) ] = sqrt [ 25 * (0.25 + 0.1667) ] = sqrt [ 25 * 0.4167 ] = sqrt [ 10.4175 ] = 3.23 t = (M1 - M2) / SE = (22 - 14) / 3.23 = 2.47 Step 4: Make the decision. We need to find the critical t-value for a two-tailed test with df = n1 + n2 - 2 = 4 + 6 - 2 = 8 at α = .05. The critical t-value is approximately ±2.306. Since our calculated t-value (2.47) is greater than the critical t-value, we reject the null hypothesis. 2. Effect Size (Cohen's d) Cohen's d = (M1 - M2) / sqrt(s^2) = (22 - 14) / sqrt(25) = 8 / 5 = 1.6 3. R-squared r^2 = t^2 / (t^2 + df) = 2.47^2 / (2.47^2 + 8) = 6.1 / (6.1 + 8) = 6.1 / 14.1 = 0.43 In conclusion, the results suggest that exposure to KUWTK has a significant effect on language functioning, with a large effect size (d = 1.6) and a moderate proportion of variance explained (r^2 = 0.43).