AI Tutor
If you try to plot music genres on a histogram or calculate the average zip code in a town, your analysis breaks down instantly.
Before you calculate a single statistic or draw a chart, you have to know what kind of raw data you are holding.

Qualitative vs. Quantitative

At the top level, all statistical data falls into one of two major categories: qualitative or quantitative.
Qualitative data (or categorical data) consists of descriptive labels, names, or categories that do not have meaningful numerical values. Quantitative data consists of numbers that represent counts or measurements where arithmetic operations actually make sense.
Numbers are not always quantitative. If you wear jersey number 23, adding it to jersey number 10 does not make jersey 33 — it is simply a qualitative label.
Once we know data is numerical, how do we distinguish between counting distinct objects and measuring smooth quantities?

Discrete vs. Continuous Data

Quantitative data splits into two distinct mathematical forms: discrete and continuous.
Discrete data can only take specific, isolated values with distinct gaps between them — usually values obtained by counting. Continuous data can take any real numerical value within a given interval, limited only by the precision of the measuring instrument.
Shoe size is a classic test: shoe sizes can be half-sizes like 8.5 or 9.0, but because you cannot have a size 8.734, shoe size is discrete.