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Independent-Samples t Test: comparing two separate groups - Coggle Diagram
Independent-Samples t Test: comparing two separate groups
Research Designs
Independent-Measures Research Design: compares two separate groups of participants, with each person participating in only one condition
Between-Subjects Design: another name for independent-measures design, where different people make up each group
Repeated-Measures Research Design: uses the same participants in multiple treatment conditions or measures them more than once
Within-Subjects Design: another name for repeated-measures design, where participants serve as their own comparison group
Independent-Measures t Statistic: determines whether the difference between two independent sample means is statistically significant
Variability & Calculations
Sample Mean Difference: the difference between the averages of two groups
Population Mean Difference: the actual difference between two population means, represented by \(\mu_1-\mu_2\)
Pooled Variance: a weighted average of two sample variances used to estimate their common population variance
Estimated Standard Error: estimates how much the difference between two sample means would vary due to random sampling
Degrees of Freedom: determines which t distribution to use; calculated as \(df=n_1+n_2-2\)
Hypothesis Testing
Null Hypothesis (H₀): assumes there is no difference between the two population means
Alternative Hypothesis (H₁): suggests that the population means differ, or that one is specifically higher or lower
Critical Value: the cutoff t score that determines whether the null hypothesis should be rejected
Statistical Significance: indicates that the observed difference would be unlikely under the null hypothesis
Two-Tailed Test: tests for a difference between groups in either direction, without predicting which mean will be higher
Effect Size
Effect Size: describes how large or meaningful a difference is, beyond whether it is statistically significant
Cohen's d: measures the difference between two means in standard deviation units
Percentage of Variance Accounted For: describes how much of the variability in scores is associated with the group difference
Coefficient of Determination (r²): an effect-size measure calculated as \(r^2=t^2/(t^2+df)\)
Treatment Effect: the difference in outcomes associated with different treatments or conditions; causal interpretation depends on the research design
Confidence Intervals & Assumptions
Confidence Interval: a range of plausible values for the true difference between two population means
Confidence Level: the long-run percentage of confidence intervals expected to contain the true population difference, commonly 95%
Homogeneity of Variance: the assumption that the two populations being compared have equal variances
Hartley's F-Max Test: compares the largest and smallest sample variances to evaluate the equal-variance assumption
Sampling Distribution of Mean Differences: the distribution of differences between sample means across all possible independent samples of given sizes