2 Meet your data!
This chapter covers
- Understanding data and what it represents
- Understanding the relationship between a sample and a population
- Common measures in describing data
In statistics, we’re often interested in structured data, typically tabular data, to work with analysis tools such as Pandas and Excel. Unstructured data, such as documents, audio, or images, where each pixel acts as a variable (or three if you count the RGB values), is often impractical for manual statistical analysis because of the sheer number of variables involved. Such data is best left to perceptual models, such as deep learning and large language models. Statistics plays a significant role in these models, but in a way that’s largely inaccessible to internal analysis. Still, we can verify the outputs with statistics.
In the examples in this chapter, we will often focus on a single dataset for a single variable, such as temperatures or exam scores. When working with real-world data, though, you will usually have tabular data with multiple variables as columns, each representing a different attribute or measurement. We will see an example of this at the end of the chapter.
Before we get into computing measures with data, let’s start with some critical qualitative questions, such as “What is data anyway?”