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t Test for 2 Independent Samples - Coggle Diagram
t Test for 2 Independent Samples
Independent Measures
Compares means from two separate groups
2 Sample Design
Uses two different samples, not the same participants
Sample Mean difference
Shows actual difference between groups
Population Mean Differnce
Expected Difference
Pooled Variance
A combined estimate of variance from two samples
Purpose
Fixes bias when sample sizes are different
Directional Hypothesis
Predicts a specific direction
Null hypothesis
States no effect or opposite direction
Limitation
cannot detect effect in opposite direction
Critical Region
located in only one tail of the distribution
Homogeneity of variance
populations must have equal variances
Pooled variance requirement
only meaningful if variances are equal
Equal variance assumption
both samples estimate the same population variance
Sample Variance effect
larger variance makes differences harder to detect
Standard error link
SE increases when variance increases
Cohen’s d
standardized measure of effect size
Independent-measures t test
compares mean difference between two separate groups