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Introduction to Inferential Statistics: What you need to know before you take regression

Course Dates and Times

Monday 29 July – Friday 2 August

09:00–10:30 & 11:00–12:30


Levente Littvay

Central European University

Open any top political or social science journal and half (if not more) of the articles are littered with tables, numbers, statistical findings. The purpose of this course is to demystify these and make you an informed consumer of quantitative research.

On this course, you will learn the basics of statistical inference and the most commonly used statistical techniques in political science research for differentiating between groups or to see if there is a difference between two variables.

The course is designed to give you an in-depth understanding of how these statistical techniques work while minimising the mathematical burden on you.

Some basic maths is necessary, but if you can add, subtract multiply and divide numbers, you will be fine.

The course provides the foundation for multiple regression and is a must for anyone wanting to take that course as long as you are not familiar or comfortable with the topics listed in this outline.

ECTS Credits for this course

2 credits (pass or fail) Attend at least 90% of course hours, participate fully in in-class activities and carry out the necessary reading and/or other work prior to and after classes.

3 credits (to be graded) As above, plus complete short daily assignments to assess the mastery of material covered in the readings or in class.

4 credits (to be graded) As above, plus complete a short, written assignment requiring integration of material covered during earlier classes. Assignments will be discussed in the final class.

Instructor Bio

Levente Littvay researches survey and quantitative methodology, twin and family studies and the psychology of radicalism and populism.

He is an award-winning teacher of graduate courses in applied statistics with a topical emphasis in electoral politics, voting behaviour, political psychology and American politics.

He is one of the Academic Convenors of ECPR’s Methods School, and is Associate Editor of Twin Research and Human Genetics and head of the survey team at Team Populism.


This course was designed for people who do not have much prior experience with statsitics or who are not totally comfortable with basic statistical concepts like hypothesis testing, p-values, standard errors, distributions, correlation, etc.

It is a one-week course providing a solid foundation for those starting from practically zero who want to work up to taking the course in multiple regression (the most commonly used statistical technique in the social sciences) in the second week.

Day 1
First we discuss basic distributions with a strong emphasis on the normal distribution (otherwise known as the bell curve) but considering others as well. We cover the types of variables and how central tendency (mean, median, mode) and variability (range, variance and standard deviation) can be measured, as these are the essential foundational building blocks of statistical inference.

Day 2
We continue by understanding how standardised scores (such as IQ, SAT, GRE and, most importantly, z-scores) can get us one step closer to comparable statistical estimates used for inference. Then we turn back to the normal distribution to see how the concepts of probability, distributions and standardised scores come together, highlighting the power of both the normal distribution and what it can tell us.

Day 3
We explore why it is not even crucial to have normally distributed variables when doing statistical inference. With our newly gained knowledge we learn to leverage statistical properties of group means and learn how to formulate (and even test) simple hypotheses using statistical inference.

Day 4
We leverage the discoveries of an old brewery employee who also revolutionised the world of statistics by allowing us to move from simple hypothesis testing of situations with too many assumptions (z test) to be realistic to testing hypotheses commonly found in the social sciences (t test and ANOVA). We explore both group comparisons, changes over time and comparisons to some gold standard value.

Day 5
We move to testing relationships between variables using correlations and we introduce bivariate regression which provides the foundation of multiple regression analysis. If time allows we explore another class of statistics which make less assumptions about distributions and learn how to answer different types of research questions with this different type of analytical technique (the Chi-Square test).


NONE – REALLY! If you can remember how to add, subtract, divide and multiply using both negative and positive numbers, you are ready to take this course.

There may be an occasional square and square root that, in this class, we are happy to calculate with a calculator.

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least two hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for two hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.

Day Topic Details
Monday Scales, Distributions, Measures of Central Tendency, Variability

Ch 1–4

Tuesday Standardsation and Probability Theory

Ch 5–6

Wednesday Central Limit Theorem and Hypothesis Testing

Ch 7–8

Thursday Multiple Forms of the t-test, Multiple Group Comparisons and Estimation

Ch 9–12 (13–14 optional)

Friday Correlation, Bivariate Regression, Intro to Nonparametric Statistics

Ch 15–16

Day Readings

Ch 1–4


Ch 5–6


Ch 7–8


Ch 9–12 (13–14 optional)


Ch 15–16

Frederick J Gravetter and Larry B. Wallnau
Essentials of Statistics for the Behavioral Sciences
Current edition is 8th but that is missing the Estimation Chapter (12 – and subsequent numbering is adjusted accordingly). Look for an earlier edition; doesn't really matter which. 

Also note that these authors also wrote Statistics for The Behavioral Sciences without the 'Essentials'. That is also a great book but its chapters are longer, more in depth and there are more chapters covering additional topics.  For this class, it is not a must, but if you prefer, feel free to use that as well.  Just cross-reference the broad chapter topics.

Software Requirements

Depending on time, I may show off some of the statistics learned in statistical software. I will use free and open source software so feel free to bring your laptop and follow along on that.

PSPP and/or R

Hardware Requirements

All three work on Mac, Linux and Windows requiring very little resources for basic stats. If your computer can run it, it is good enough for us.


There is a long list of introductory statistics books out there. I selected the textbook I found the most accessible to graduate students with a slight aversion to math and quantitative research. Here are a two more I know to be widely acclaimed.

David Freedman, Robert Pisani and Roger Purves
Current edition is 4th (2007) but any edition would be good to reference
W. W. Norton & Company

Michael Lewis-Beck (1995)
Data Analysis: An Introduction


Recommended Courses to Cover Before this One

None. This is the first course to take.

Recommended Courses to Cover After this One

Introduction to Logistic Regression