1. Statistics in SPSS Lecture 7
Petr Soukup, Charles University in Prague

2. T-tests 2

3. T-tests Statistical tests for means
Can be used for proportions as well
Three tests:
for one sample (rare usage)
Two independent samples (the most common)
Test for paired (dependent) samples 3

4. T-tests Requirements: Cardinal variable
Normally distributed (small samples)
For big sample (more than 30 units) normality is not necessary
PARAMETRIC TESTS (it is necessary to follow some requirements) 4

5. T-tests Analyze-Compare Means 1. example – ONE SAMPLE :
for mean (income) and for proportion (participation in the last election)
2. example – TWO IND. SAMPLES :
no difference for participation in the last election between men and women
Note: Cohen’s d for two independent samples (effect size, measurement of substantive significance of results) 5

6. TESTS & CI 6

7. Confidence interval (CI) and testing
CI can answer the same question as the test:
Can be the effect generalized to the population (from the sample)?
CI can do more:
Estimate unknown parameter for population by the range, so we know the size of the effect !
Warning: Testing is only binary process –statistically significant vs. statistically insignificant 7

8. CI and testing Comparison of t-test and CI for mean
Reco: Use CI instead or with the test 8

9. ALTERNATIVES TO T-TESTS (NON-PARAMETRIC TESTS)9

10. Alternatives to T-tests
data
Parametric test
Nonparametric
a) Two-ind. Samples
Two ind. sample t-test
Man-Whitney test
b) Two related samples (paired)
Paired t-test
Wilcoxon test
c) More ind. Samples
Analysis of variance
Kruskal-Wallis test
d) More relateds samples
Friedman test 10

11. HW 11

12. HW6
Try to test mean (one cardinal variable) by t-test and use the same test also for proportion (one binary variable). Interpret results. 12

13. Thanks for your attention13