## Inferential Statistics

Learn about inferential statistics, and how they are used and misused in the social and behavioral sciences. Learn how to critically evaluate the use of inferential statistics in published research and how to generate these statistics yourself, using freely available statistical software.

In this course we will treat inferential statistics in detail. You will learn about all the statistical tests that are typically treated in a full bachelor program in any quantitatively oriented social or behavioral science. We will consider some questionable research practices (treated in the course Quantitative Research Methods) and see how they work in more detail.

Inferential statistics are concerned with inferences based on relations (differences, correlations) found in the sample, to relations in the population. In other words, inferential statistics help us to decide whether a pattern we find in the sample is likely to hold for all people or groups that our hypothesis applies to. Stated yet another way, inferential statistics help us decide whether the differences between groups or correlations between variables that we see in our data are strong enough to conclude that our predictions were confirmed and our hypothesis is supported.

We will consider a large number of statistical tests and techniques that help us make these inferences for different types of data and different types of research designs. For each individual statistical test we will consider how it works, for what data and design it is appropriate and how results should be interpreted.

We will look at z-tests for 1 and 2 proportions,  McNemars test for dependent proportions, t-tests for 1 mean (paired differences) and  2 means, the Chi-kwadraat test for independence, Fisher’s exact test, simple regression (linear, exponential en logistic) and multiple regression, one way and multi-way analysis of variance, and non-parametric tests (Wilcoxon, Kruskal-Wallis, sign test,  signed-rank test).

Before we look at the individual tests, we will consider the basic principles of significance testing: probability distributions, p-value, significance level, power and type I and type II errors. If these terms mean nothing to you, don't worry, they will all be explained!

You will not only learn the basics of significance testing and the most commonly used statistical tests, you will also learn how to choose which test to use, how perform these tests using freely available software and how to interpret your own results and the results of others critically.