## Inferential Statistics

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Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population.

We will start by considering the basic principles of significance testing: probability distributions, p-value, significance level, power and type I and type II errors. Then we will
consider a large number of statistical tests and techniques that help us make 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. You will also learn how to perform these tests using freely available software.

For those who are already familiar with statistical testing: We will look at z-tests for 1 and 2 proportions,  McNemar's test for dependent proportions, t-tests for 1 mean (paired differences) and 2 means, the Chi-square 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).

#### 课程大纲

Inferential statistics help us decide whether our data support our research hypothesis. In this course we will treat inferential statistics in detail. You will learn about the basics  of significance testing and all the statistical tests that are typically treated in a full bachelor program in any quantitatively oriented social or behavioral science. If the terms below mean nothing to you, don't worry, they will all be explained!

You will learn when and how to use these statistical techniques, how to perform them using statistical software and how to interpret your own results and the results of others critically. We will also consider some questionable research practices (treated in the course Quantitative Research Methods) and see how statistics can be misused in more detail.

Week 1: Significance testing

• basic concepts
• tests for one mean and one proportion
• tests for two means and two proportions
• type I and II errors and power
• quiz and warm-up assignments (not graded)

Week 2: Categorical association

• testing for categorical association
• interpreting the association
• Fisher's exact test
• quiz and small assignment (graded)

Week 3: Simple regression

• basic concepts
• simple linear regression
• exponential regression
• quiz and paper on week 1 & 2 due (graded)

Week 4: Multiple regression

• multiple linear regression
• logistic regression
• quiz and small assignment (graded)

Week 5: Analysis of variance

• basic concepts
• one-way analysis of variance
• two-way analysis of variance
• analysis of variance and regression
• quiz and paper on week 3 & 4 due (graded)

Week 6: Non-parametric methods

• comparing two independent groups
• comparing two dependent groups
• comparing more than two independent groups
• quiz and small assignment (graded)

Week 7: Study week

• time to work on last paper

Week 8: Exam week

• paper on week 5 & 6 due (graded), final exam (graded) and course evaluation

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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.