Statistics I

The aim of this course is to learn what kind of conclusions can be drawn from numerical data. The basics of statistics and statistical tests will be covered so that you will know how to make simple statistical analyses independently. This course will focus on two topics: One with general issues of statistical inference, and the other with specific statistical tests. The general topics refer to the basis of statistical inference, independent from the type of analysis you are performing, and is mainly theoretical. Understanding of these general issues gives you a better sense of what you are doing across a wide variety of statistical analyses. We will focus on this mainly in the beginning of the course. Later in the course we will look at specific statistical tests: When are they to be used, why, and how.

In this course you will learn about statistical methods and inference necessary to analyze data and test the relationships between variables. To get to this point you will need a basic understanding of the principles of statistical reasoning. These are not so much mathematical principles, but rather logical principles. As a consequence the emphasis in this course lies on the verbal understanding of this logic. You will also learn how to apply this knowledge in practice, in the form of different statistical tests. However, all these tests are expressions of the same underlying logic, applied in different situations.

One of the main reasons to teach you all this is because it enables you to test your ideas, notions and theories. Any scientific theory has to be put to the test to see if its predictions are correct. An important part of this involves translating research questions into testable hypotheses which can be verified by applying the appropriate statistical techniques. Understanding of statistical inference makes it possible to make general conclusions about larger populations of people on the basis of research outcomes obtained from a relatively small number of tested participants. This course therefore helps you in forming a deeper understanding of the (empirical) scientific method, and complements knowledge of research methods.

A major goal of this course is to prepare you to become an independent researcher of course. But you will benefit from having knowledge of statistics in other, more direct ways too. It will make it easier for you to read, understand and judge scientific writing for instance. Because of your increased skills in statistical reasoning it will become easier to avoid common cognitive biases and logical thinking errors, which greatly helps with drawing correct conclusions. You will be much better able to distinguish between common sense beliefs and scientific beliefs, which helps a lot with seeing the flaws in bogus or pseudo science and pop-science often found in magazines. So this course in general also increases your critical thinking skills. Additional, forms of more professional skills you will acquire include an increased ability to communicate and explain research setups and findings, using numbers and graphs, and a better understanding of the construction, and use of psychological tests.

1. Introduction / measures of center

• Pages 1-16 of chapter 1 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

• Pages 16-14 of chapter 2 of Howell, D. C. (2002). Statistical methods for Psychology, 5th ed. Duxbury, Pacific Grove, CA.

2. Measures of variability

• Pages 53-61 of chapter 3 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

3. Z -> standardizing scores & the normal distribution

• Pages 71-71 of chapter 4 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

• Chapter 6 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

4. Sampling, sampling distribution and probability

• Pages 142-156 from chapter 7 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

5. Confidence intervals

• Pages 434-442 of chapter 6 of Moore, D. D., McCabe, G. P., & Craig, B. (2014). Introduction to the Practice of Statistics, 8th ed. W. H. Freeman, NY.

6. Effect size and the t-distribution

• Sullivan, G. M., & Feinn, R. (2012). Using effect size-or why the P value is not enough. Journal of graduate medical education, 4(3), 279-282.

• Remainder of chapter 8 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

7. Testing a hypothesis

• Pages 453-468, 476-481 of chapter 6 from Moore, D. D., McCabe, G. P., & Craig, B. (2014). Introduction to the Practice of Statistics, 8th ed. W. H. Freeman, New York, NY.

8. Significance, Type I & II errors, and power

• Pages 168-172, 176-181 of chapter 8 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

9. Two-sample t-tests

• Pages 324-336 of chapter 9 of Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.

10. ANOVA

• Chapter 10 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

11. Factorial ANOVA

• Chapter 12 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

12. Correlation

• Pages 85-107 of chapter 5 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

• Pages 248-254 of chapter 9 of Howell, D. C. (2002). Statistical methods for Psychology, 5th ed. Duxbury, Pacific Grove, CA.

• Pages 166-179 of chapter 6 of Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.

13. Regression

• Pages 108-115 of chapter 5 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

• Pages 197-205 of chapter 7 of Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage

14. Chi-square

• Chapter 13 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

15. Non-parametric alternatives to parametric tests

• Chapter 14 of Spatz, C. (2005). Basic statistics: Tales of distributions, 8th ed. Wadsworth, Belmont, CA.

- Knowledge how to use different statistical methods to describe, investigate and test relationships between variables

- The ability to solve research questions by translating them into testable hypotheses, applying the appropriate tests, and making the correct inferences to come to the most appropriate conclusions

- Understanding of statistical inference

- Understanding of the logic and reasoning underlying statistical principles, such as probability theory

- Understanding of the use of statistical methods in the broader context of the empirical scientific methods

a) Fourteen short in-class tests, worth 2 points each.

b) Mid-term exam, 36 points.

c) Final exam, 36 points.

97 or more = 5!

92-96 = 5

84-91 = 4.5

76-83 = 4

68-75 = 3.5

60-67 = 3

below 60 = 2 (fail)

Attendance is obligatory for both lectures and exercise classes. No more than 2 of each can be missed without valid excuse. For the exercises, missing more than 4 classes overall leads to course failure. For the lectures missing more than 50% leads to course failure.

Students must respect the principles of academic integrity. Cheating and plagiarism (including copying work from other students, internet or other sources) are serious violations that are punishable and instructors are required to report all cases to the administration.

This also applies to the exercises: do not copy solutions to exercises from a colleague. Students being caught copying work will be immediately excluded from the course (=fail) and referred to the University’s disciplinary commission.