* Recognize the importance of data collection, identify limitations in data collection methods and other sources of statistical bias, and determine their implications and how they affect the scope of inference.
* Use statistical software to summarize data numerically and visually, and to perform data analysis.
* Have a conceptual understanding of the unified nature of statistical inference.
* Apply estimation and testing methods to analyse single variables or the relationship between two variables in order to understand natural phenomena and make data-based decisions.
* Model numerical response variables using a single explanatory variable or multiple explanatory variables in order to investigate relationships between variables.
* Interpret results correctly, effectively, and in context without relying on statistical jargon.
* Critique data-based claims and evaluate data-based decisions.
* Complete two research projects: one that employs simple statistical inference and another that employs more advanced modelling techniques.

PLEASE NOTE THAT THE INFORMATION ABOUT THE TEACHING AND ASSESSMENT METHOD(S) USED IN THIS COURSE IS WITH RESERVATION. THE INFORMATION PROVIDED HERE IS BASED ON THE COURSE SETUP PRIOR TO THE CORONAVIRUS CRISIS. AS A CONSEQUENCE OF THE CRISIS, COURSE COORDINATORS MAY BE FORCED TO CHANGE THE TEACHING AND ASSESSMENT METHODS USED. THE MOST UP-TO-DATE INFORMATION ABOUT THE TEACHING/ASSESSMENT METHOD(S) WILL BE AVAILABLE IN THE COURSE SYLLABUS. In our course, we will focus on the following topics:
* Methods of data collection, and types of data.
* Descriptive statistics: describing important characteristics of populations or samples by numerical methods as the mean, median, mode (measures of central tendency), variance and standard deviation (measures of spread) as well as by graphical methods, like histograms, bar charts or Box-and-Whiskers displays.
* Probability theory, as an introduction to random variables.
* Discrete random variables and the most important discrete probability distribution: the binomial distribution; continuous random variables and two continuous probability distributions: the uniform and the normal distribution.
* Sampling theory, as the foundation of inferential statistics, or inductive reasoning.
* The construction of confidence intervals to estimate unknown population parameters.
* Hypothesis testing for both the proportion and means cases.
* Regressions analysis and ANOVA: the investigation of relationships.

Teaching methods:
Tutorial groups.

Assessment methods:
Final exam and intermediate quizzes.

* OpenIntro Statistics, 4th Edition, 2019, 422 pages, by David Diez, Çetinkaya-Rundel, Christopher D. Barr.
This is an open access text, you can download the text from the website of OpenIntro: https://www.openintro.org/stat/textbook.php?stat_book=os

None.

Statistics; descriptive statistics; probability models; random variables; hypothesis testing; inferential statistics; regression analysis; analysis of variance.