At the end of this course, students should be familiar with the basic concepts of inferential statistics, and will be able to perform basic statistical analysis in a variety of scenarios. In most scientific research, researchers have to deal with the problem of drawing conclusions about some population characteristic of interest, relying only on a sample of observations from that population. Inferential statistics is a way to tackle this problem. This course starts by covering the foundations of inferential statistics, emphasizing the logic behind the statistical reasoning process. This logic is then employed to explain a number of widely used applied statistical methods: ANOVA, Chi-square, Nonparametric Wilcoxon tests and multiple regression. Students will learn how to run each of these applied tests using the statistical software package SPSS. Additionally, they will learn how to determine the minimal number of observations needed to be able to show, with a fixed probability, a specified research hypothesis.
• To enhance students’ understanding of the basics of inferential statistics. • To broaden the scope of statistical methods that students are acquainted with by introducing a number of widely used applied tests that were not covered in PRA1002. • To understand how researchers determine required sample sizes for a number of (simple) designs and to be able to apply these methods. • To familiarize students with statistical software, so that they can independently run the analyses that are covered in this course and are able to correctly interpret the corresponding output.