ONLINE COURSE
Statistical thinking is not only the key skill to analyse data in scientifically objective way, but also the key skill of the digital era. The course introduces the key statistical and non-statistical concepts to develop modern statistical thinking and it provides references for further learning.
It is a thinking methodology of the modern statistical science, which enables us to analyse data in a scientifically objective way.
The development of modern statistical science goes back to 20th century with classical contributions such as Fisher’s randomisation (1925), Neyman’s modern concept of a confidence interval (1935) and Rubin’s Causal Model (Holland, 1986). These contributions had a significant impact on development of modern sampling theories, statistical inference and approaches for handling missing data.
Modern statistical science emphasizes the importance of carefully designed studies and for that reason requires from researchers to be familiar with statistical thinking that is founded on modern statistical science, i.e., modern statistical thinking.
The course covers the key statistical and non-statistical concepts of a newly developed approach for developing modern statistical thinking (Kolar, 2019). The new approach defines statistical thinking as a conscious thought process that is based on statistical concepts of modern Sampling theory, Missing data, understanding of the usefulness of Descriptive Statistics and challenges in conclusion-making of Inferential Statistics.
The central element of this new approach is causal thinking. Students get familiar with the science behind causal thinking as well as with approaches to analyse causal relationships.
This is a fully self-paced course that consists of video lessons, quizzes, exercises and a final assignment for those who wish to obtain a course certificate.
Keep in mind that completing the final assignment enables you to deepen your knowledge, so it is not just a paper. The course instructor provides you with feedback on your final assignment.
The material has been previously used for lectures, seminars and workshops at different venues, including University of Helsinki, Sigmund Freud University, Uppsala University and Tsinghua University.
Dr. Ana Kolar holds a PhD in Statistics. Her PhD advisor and statistical guru is one of the greatest statisticians of today – Emeritus Professor Dr. Donald B. Rubin from Harvard University. Ana is able to introduce and explain complex topics in a simple manner and she strives towards an educational approach that is based on experiential learning. Read more about Ana here.
After completing this course, you will become familiar with the key statistical and non-statistical concepts required to develop modern statistical thinking – the thinking that will enable you to analyse in scientifically objective way.
During the first class we learn about the key concepts of this new approach to develop statistical thinking. We explore the key statistical and non-statistical concepts required for analysing data in scientifically objective way. We look at the skills and attitudes that are required in order to fully master the modern statistical thinking.
3 videos, 1 exercise and 1 quiz to complete.
During the second class we introduce the concept of causality and its impact on scientific enquiry. We learn about the science behind causal thinking and the impact that causal thinking has on designing studies, analysing data and conclusion-making.
3 videos, 1 exercise and 1 quiz to complete.
During the third class we become familiar with the important role of sampling theory which enables us to develop understanding of the missing data mechanisms – the essential tools for being able to deal with biases.
3 videos, 1 exercise and 1 quiz to complete.
During the fourth class, we consolidate the knowledge from the previous classes and look at Descriptive Statistics. We talk about different types of biases and the impact that bias has on usefulness of data-insights presented with Descriptive Statistics.
We explore circumstances that cause interpretations of Descriptive Statistics to be misleading and we end class discussing questions that should always be asked before trusting data-insights presented with Descriptive Statistics.
3 videos, 1 exercise and 1 quiz to complete.
During our last class, we continue where we left with the previous class and introduce the meaning of Inferential Statistics, its challenges and frequent misuses. Complexities of Inferential Statistics are discussed through number of assumptions that are required to be satisfied in order for Inferential Statistics to produce trustworthy data insights.
3 videos, 1 exercise and 1 quiz to complete.
You can access the course material for 12 months from the day of enrolment, whatever comes later. This access limitation is due to course material being regularly updated. Upgrades to a new edition are possible.
Yes, you can cancel the course anytime during your Class 1 lessons, or before accessing material of Class 2 lessons, and you will get a full refund. The cancellation needs to be done in writing by emailing to admin@tarastats.com in 30 days after enrolment or the official start of the course (whichever comes later). Cancellations after this period of time will not be possible.
This is a fully self-paced online course that includes interaction with the course instructor.
This course starts on the 5th of February 2024.
The fees are inclusive of 24% VAT.
Do you wish to be informed of the dates for our upcoming courses? Leave your email below and we will be in touch.
Tarastats Statistical Consultancy
Fredrikinkatu 61A, 6th floor
00100 Helsinki, Finland
Business-ID 2727413-2
info@tarastats.com