Chapter 6: Modelling Variations
Learning Objectives
After reading this chapter, you should be able to:
- describe the basic equation for statistical models (data=model + error)
- distinguish between a population and a sample, and between population parameters and sample statistics
- describe the concepts of sampling error and sampling distribution
- describe how the Central Limit Theorem determines the nature of the sampling distribution of the mean.
In this chapter, we will delve into big ideas in statistics–Modelling, Uncertainty and Sampling from Population. As mentioned in Chapter 1, one of the fundamental activities in statistics is creating models that can summarise data using a small set of numbers, thus providing a compact description of the data.
Another foundational idea in statistics is that we can make inferences about an entire population based on a relatively small sample of individuals from that population. In this chapter, we will introduce the concept of statistical sampling and discuss why it works. As Charles Wheelan, the author of Naked Statistics aptly describes the process of inference as using data from the “known world” to make informed inferences about the “unknown world.” There will always be uncertainties in our data and this could be due to the fact that we usually sample from a population. Therefore, to understand how we can use statistics to make these inferences, we will also talk about sampling, the central limit theory and null hypothesis testing.
