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Lecturer(s)
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Dostál Daniel, PhDr. Ph.D.
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Course content
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The course consists of the following areas: 1. data matrix, statistical hypothesis tests I 2. statistical hypothesis tests II, indicators of effect si 3. parametric tests I (t-test, Welch test) 4. parametric tests II (F test, Pearson's correlation coefficient tests) 5. analysis of variance, the multiple testing issue, post-hoc tests 6. assumptions of parametric tests and their validation 7. power analysis 8. pivot tables, goodness of fit tests 9. nonparametric tests I 10. nonparametric tests II (Kruskal-Wallis test, Spearman correlation coefficient) 11. selection of statistical test
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Learning activities and teaching methods
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Lecture, Monologic Lecture(Interpretation, Training), Work with Text (with Book, Textbook), Demonstration, Projection (static, dynamic)
- Homework for Teaching
- 35 hours per semester
- Preparation for the Exam
- 40 hours per semester
- Attendace
- 25 hours per semester
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Learning outcomes
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The main topic the course are statistical hypothesis tests. Students will understand the logic of inferential statistics and the principle of basic statistical tests. In addition to statistical significance and the concept of practical significance and power of a statistical test will be introduced. Finally, students will get familiar with the weaknesses and limitations of the null hypothesis testing in research practice. Acquired skills will be practiced in statistical software STATISTICA 13.3.
By completing this course, students will gain the ability to formulate a statistical hypothesis, select and perform the appropriate statistical test, and interpret the results. Furthermore, students understand the concepts of the effect size, statistical power, power analysis etc.
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Prerequisites
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Knowledge of the basics of probability theory and mathematical statistics, descriptive statistics and principles statistical estimates. Recommended (although not necessarily) is a pre-requisite course Statistical Methods in Psychology 1.
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Assessment methods and criteria
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Mark, Written exam, Student performance
The course requires a credit and a examination. To receive credit, students must complete homework assignments in due time (at least 80% of the points are required for successful completion). The exam is a test, which again consists of individual work with data (mainly the selection, evaluation and interpretation of a statistical test). The test consists of eight tasks, which will always (or almost always) relate to the given data table. The tasks will be arranged in four pairs. The first task of the pair consists of selecting, performing and interpreting the test. The second task of each pair will involve some more advanced knowledge (e.g., calculating the effect size indicator, analyzing the power of the test, calculating the p-value from a given distribution, comparing two sample correlation coefficients, converting a two-sided p-value to a one-sided p-value...). Key words: alternative hypothesis and null hypotheses, test statistics, p-value, significance level, critical region, one-sided and two-sided test, the error of the first and second type, effect size, statistical power, relative efficiency, one sample and paired samples t-test, t-test for two independent samples and Welch test, F-test, tests the Pearson correlation coefficient (using the t-distribution and using Fisher's Z transformation), multiple testing problem, ANOVA and Welch ANOVA, Tukey and Scheffé test, the goodness of fit test, the test of independence (homogeneity) and Fisher factorial test, McNemar test, sign test, Wilcoxon test, Mann-Whitney U-test, Spearman correlation coefficient, Kruskal-Wallis one-way analysis of variance
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Recommended literature
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Daniel Dostál. (2016). Statistické metody v psychologii.
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