🗓️ Sessions 15 and 16: Sampling

A central concept in data science - and in applied statistics more generally - is that of sampling. This refers to the strategy of using (small) samples to learn about a (large) population. For example, if you wanted to understand the effect of TV advertising on the consumer behaviour of young men in Germany, you could study the whole population of young men in Germany. But since this is usually not feasible, you would rather take a sample of young men, study their behaviour and then generalise to the whole population. In this session we will discuss when and how this is possible. In this context, we will also learn about the concept of Monte Carlo simulations and two central concepts of probability theory underlying applied statistics: the central limit theorem and the law of large numbers, both of which underlie much of modern sampling theory.

👨‍🏫 Lecture Slides

Either click on the slide area below or click here to download the slides.

Lecture code

• Extensive solution for the exercise on the average height of EUF students
• Solution for the exercise on terminology
• Explanation of the Central Limit Theorem and the corresponding exercise

🎥 Lecture videos

So far, there are no learning videos available for this lecture.

📚 Mandatory Reading

• Tutorial on sampling
• Chapter 7 in Ismay and Kim (2020).

✍️ Coursework

• Do the exercises Sampling from the DataScienceExercises package

Quick code for starting the exercises

learnr::run_tutorial(
  name = "Sampling", 
  package = "DataScienceExercises", 
  shiny_args=list("launch.browser"=TRUE))

References

Ismay, C. and Kim, A. Y.-S. (2020) Statistical inference via data science: A ModernDive, into R and the tidyverse, Boca Raton: CRC Press, Taylor and Francis Group, available at https://moderndive.com/index.html.