Statistics for Psychologists I
Full course description
This course consists of two parts. During the first part of the course, students will study the foundations of inferential statistics. A great deal of emphasis will be placed on the logic behind the statistical reasoning process. During the second part of the course, students will be familiarised with several statistical techniques often used in the field: t-tests, ANOVA and X2 tests. In the parallel SPSS practical, students will be given the opportunity to apply these techniques to several real data sets. The subjects covered in the second part of this course will consistently be linked to the basic terms that were explained in the first part of the course.
- are able to specify and explain relevant concepts that are central in inferential statistics, including random experiment, sample space, events, (un-)conditional probability, statistical (in)dependence, random variables, probability distribution, expected value and standard deviation, density curve, simple random sampling, parameters and (unbiased) estimators, population distribution, distribution of sample scores, sampling distribution, standard error, central limit theorem, null- and alternative hypothesis, one vs. two-tailed test, test statistic, p-value, significance level, power, Type I- and Type II-errors, confidence interval, population and sample proportion;
- are able to explain and apply specific statistical techniques, such as z-test, t-tests, ANOVA, X2-goodness of fit test, X2-test for contingency tables, and they can interpret relevant output of these tests;
- are able to specify the assumptions of statistical tests that were discussed in this module as well as the conditions for, robustness against violations of these assumptions and are able to apply this knowledge when analysing data.