Independent T-Test and One-Way ANOVA Overview
1. Independent Sample T-test: Compares performance of two different participant samples.
2. Null Hypothesis: States no difference exists between two groups.
3. Alternative Hypothesis: States a significant difference exists between groups.
4. Effect Size (ES): Proportion of variance explained by independent variable.
5. Cohen's D: Point estimate statistic for measuring effect size.
6. Levene's Test: Tests equality of variances between samples.
7. Significance Level: Probability threshold for rejecting the null hypothesis.
8. Directional hypothesis (one-tailed): Predicts the direction of the difference between groups.
9. Nondirectional Hypothesis (two-sided): Predicts a difference without specify- ing direction.
10.Degrees of Freedom (df): Number of independent values in a statistical calcu- lation.
11.One-Sample T-Test: Compares sample mean to population mean.
12.Independent Groups T-Test: Compares two independent groups' means.
13.Dependent Groups T-Test: Compares means from related groups.
14.APA Format for T-Test: t(df) = T-Stat, p = xx
15.Type I Error: Rejecting null hypothesis when it is true.
16.Type II Error: Failing to reject null hypothesis when it is false.
17.Sample Size: Number of participants in a study affecting power.
18.Critical Value (cv): Threshold for determining significance in hypothesis testing.
19.Two-Tailed Test: Tests for differences in both directions.
20.One-Tailed Test: Tests for a difference in one specific direction.
21.Statistical Significance: Likelihood that a result is not due to chance.
22.Variance: Measure of data spread in a distribution.
23.Independent t-test: Compares means of two independent groups.
24.Grouping Variable: Variable that separates participants into groups.
25.Dependent Variable: Outcome measured by the researcher.
26.Independent Variable: Variable manipulated by the researcher.
27.Subject Variable: Characteristics of participants, like gender.
28.One-Way ANOVA: Compares means of three or more groups.
29.Interval-Ratio Data: Data that measures magnitude, like GPA.
30.Levels of Independent Variable: At least two conditions of the IV.
31.Variability Between Conditions: Differences in scores across different condi- tions.
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1. Independent Sample T-test: Compares performance of two different participant samples.
2. Null Hypothesis: States no difference exists between two groups.
3. Alternative Hypothesis: States a significant difference exists between groups.
4. Effect Size (ES): Proportion of variance explained by independent variable.
5. Cohen's D: Point estimate statistic for measuring effect size.
6. Levene's Test: Tests equality of variances between samples.
7. Significance Level: Probability threshold for rejecting the null hypothesis.
8. Directional hypothesis (one-tailed): Predicts the direction of the difference between groups.
9. Nondirectional Hypothesis (two-sided): Predicts a difference without specify- ing direction.
10.Degrees of Freedom (df): Number of independent values in a statistical calcu- lation.
11.One-Sample T-Test: Compares sample mean to population mean.
12.Independent Groups T-Test: Compares two independent groups' means.
13.Dependent Groups T-Test: Compares means from related groups.
14.APA Format for T-Test: t(df) = T-Stat, p = xx
15.Type I Error: Rejecting null hypothesis when it is true.
16.Type II Error: Failing to reject null hypothesis when it is false.
17.Sample Size: Number of participants in a study affecting power.
18.Critical Value (cv): Threshold for determining significance in hypothesis testing.
19.Two-Tailed Test: Tests for differences in both directions.
20.One-Tailed Test: Tests for a difference in one specific direction.
21.Statistical Significance: Likelihood that a result is not due to chance.
22.Variance: Measure of data spread in a distribution.
23.Independent t-test: Compares means of two independent groups.
24.Grouping Variable: Variable that separates participants into groups.
25.Dependent Variable: Outcome measured by the researcher.
26.Independent Variable: Variable manipulated by the researcher.
27.Subject Variable: Characteristics of participants, like gender.
28.One-Way ANOVA: Compares means of three or more groups.
29.Interval-Ratio Data: Data that measures magnitude, like GPA.
30.Levels of Independent Variable: At least two conditions of the IV.
31.Variability Between Conditions: Differences in scores across different condi- tions.
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