Statistics Fundamentals (5/9) Sampling

Welcome to Statistics Fundamentals 5, Sampling. This course is for beginners who are interested in statistical analysis. And anyone who is not a beginner but wants to go over from the basics is also welcome!

This series consists 9 courses, including this one. This course contains theoretical explanations of sampling methods, experiment design, and Python coding tutorials. They cover Python basics and thus are easy to follow. But installation and preparation of related environments are not covered in this course. If you are an absolute beginner in statistics, I recommend taking our first course named Introduction.

When we collect data for statistical analysis, we usually cannot collect data from all the research objects. For example, when we want to know the current teenager’s personality, usually, we cannot data from teenagers worldwide. So, usually, as the second-best, we collect data from the subset of all the object. The subset is called a sample. Then, we analyze the sample data and based on the results, we make inference about all the research objects. It is called inferential statistics. It enables us to draw some conclusions from sample data without seeing the entire object. However, to make such an inference effectively, the sample should represent the entire object as accurately as possible. So, how to collect sample data is very important in inferential statistics.

This course covers the following topics. Starting with the difference between population and sample, we go on to the various survey and sampling methods. Knowledge related to them is essential to statistical inference. Then, you will learn the Law of Large Numbers, and the Central Limit Theorem. They are critical for understanding the theoretical foundation of hypothesis testing. In the last part, you will learn how to design experimental studies and observational studies. I’m looking forward to seeing you in this course.