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Psyc Stats Exam 3
| Question | Answer |
|---|---|
| When do you use an independent samples t-test? | When comparing the means of two unrelated groups (different participants in each). |
| What type of variables does an independent t-test require? | IV = nominal (2 levels), DV = interval/ratio. |
| What are the null and alternative hypotheses? | H₀: μ₁ = μ₂ Hₐ (two-tailed): μ₁ ≠ μ₂ Hₐ (one-tailed): μ₁ > μ₂ or μ₁ < μ₂ |
| Key assumptions of independent t-test? | Normality, independence, equal variances (homogeneity). |
| Formula for independent t-test df? | df = (n₁ − 1) + (n₂ − 1) |
| Effect size independent t-test? | Cohen’s d = (M₁ − M₂) / pooled SD |
| When to use a dependent t-test? | Same participants measured twice OR matched pairs. |
| Null hypothesis for dependent t-test? | H₀: μᴰ = 0 (mean difference = 0) |
| df formula for dependent t-test? | df = nᴰ − 1 |
| Effect size for dependent t-test? | d = mean difference / SD of difference scores |
| Key assumptions of dependent t-test? | Normality & pair independence |
| What is a confidence interval? | A range of plausible values for a population parameter. |
| General confidence interval formula? | Point estimate ± (critical value × standard error) |
| What affects width of CI? | 1. Confidence level (higher → wider) 2. Sample size (larger n → smaller SE → narrower CI) |
| Critical value depends on | 1. Confidence level (e.g., 95% → t or z for .025 in each tail) 2. df for t-distribution |
| A confidence interval is for... | 1. One mean (z or t) 2. Two independent means (difference of means) 3. Two related means (difference score CI) |
| Type I probability/error? | (α), Rejecting a true null (false positive). |
| Type II probability/error? | (β), Failing to reject a false null (missed effect). |
| What increases power? | Larger n, bigger α, larger effect size, lower variability, & stronger designs |
| Power (1 − β) | Probability of correctly rejecting a false H₀. Goal: Power ≥ .80 |
| Range of r? | −1.00 to +1.00 |
| What does magnitude tell you? | Strength of relationship. |
| What does sign tell you? | Direction (positive or negative). |
| What does it mean if r is near 0? | Weak or no linear relationship. |
| Pearson’s r | Measure of linear relationship between two interval/ratio variables. |
| What does it mean if r is near 1? | Strong linear relationship |
| When do you use One-way Between Subjects ANOVA? | Comparing 2+ independent groups. |
| F-statistic formula? | F = MSbetween / MSwithin |
| One-way Between Subjects ANOVA df formulas? | dfᵦ = k − 1 dfᵥ = N − k |
| Effect sizes for an One-way Between Subjects ANOVA ? | η² (eta squared) and ω² (omega squared) |
| Purpose of One-way Between Subjects ANOVA? | Test whether at least one group mean differs. |
| What does an ANOVA effect size measure? | Percent of variance in the DV that is explained by the levels of the IV. |
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