STAT3220 Key Concepts of Modern Statistical Thinking (5 cr)
Cooperation network course
Network: Vaasa Higher Education Consortium
Network: Cross-institutional studies in advanced courses in mathematics and statistics
This course is offered through the Network for Advanced Studies in Mathematics. These studies are available for the following degree students:
- Bachelor's Degree Programme in Mathematics
- Master's Degree Programme in Mathematics
- Bachelor's Degree Programme in Mathematics (Subject Teacher)
- Master's Degree Programme in Mathematics (Subject Teacher)
- Bachelor's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
- Master's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
- Doctoral Programme in Mathematics and Statistics
- Doctoral Programme in Mathematics and Science (Specialication in Mathematics)
Description
This course equips students with understanding of the key statistical and non-statistical concepts that are required for development of modern statistical thinking - the thinking that enables us to analyse data in a scientifically objective way.
Students learn about the science behind causal thinking, which enables us to become familiar with influential non-statistical biases that are regularly transmitted to statistical biases, making data-insights prone to bias of unknown dimensions. Furthermore, students learn about omnipotence of statistical assumptions and their impact on credibility of data-insights. Students also learn about ways to assess trustworthiness of presented data-insights.
In order for data to provide trustworthy data-insights, modern statistical science emphasises the importance of a carefully planned and executed study design. During this course students learn what the study design consists of, and how quality of the study design influences analysis of data and trustworthiness of data-insights. In line with this, students learn about sampling theory, missing-data mechanisms, how to handle missing data, and the impact that missing data has on usefulness of interpretations of Descriptive Statistics and Inferential Statistics. Furthermore, students become familiar with labyrinths of Inferential Statistics and learn about differences between predictive analytics, causal effect analytics and impact evaluations.
Learning outcomes
By the end of this course, students will be able to:
- Explain key statistical and non-statistical concepts that underpin modern, objective data analysis
- Apply causal thinking to identify and critically assess biases affecting data insights
- Evaluate the role and impact of statistical assumptions on the credibility of findings
- Assess the trustworthiness and limitations of presented data insights
- Design and justify a study framework that supports reliable and valid conclusions
- Explain the principles of sampling theory and their impact on data quality
- Identify and handle missing data using appropriate methods
- Analyse how missing data influences both descriptive and inferential statistics
- Distinguish between predictive analytics, causal effect estimation, and impact evaluation
- Critically interpret statistical results within the context of study design and data limitations
Description of prerequisites
An introductory statistics course or introductory experience with applied statistics is recommended.