Overview
This lecture introduces the two main types of data in statistics—categorical and quantitative—and discusses how to distinguish between them, which is foundational for analyzing data correctly.
Types of Data in Statistics
- There are two main types of data: categorical (qualitative) and quantitative.
- Knowing the type of data is crucial because analysis methods depend on it.
Categorical (Qualitative) Data
- Categorical data is made up of labels or words that describe people or objects.
- Examples include state of residence, yes/no questions, or month of birth.
- Sometimes numbers are used as placeholders for words (e.g., 1 = Yes, 0 = No), but these are still categorical if not measuring/counting.
- Zip codes and identification numbers (e.g., student ID) are numbers used for labeling, not measurement, and are treated as categorical.
Quantitative Data
- Quantitative data consists of numbers that measure or count something.
- These numbers have units (e.g., height in inches, number of cars) and averages make sense.
- Typical quantitative data includes measurements (height, weight) and counts (number of cars in a lot).
How to Distinguish Between Data Types
- If data is made up of words or numbers standing for categories, it's categorical.
- If data involves numbers with units or where calculating an average is meaningful, it's quantitative.
- Don't treat numbers that identify or label (e.g., ZIP codes, IDs) as quantitative.
Key Terms & Definitions
- Categorical Data — Data consisting of labels or words that describe qualities or characteristics.
- Quantitative Data — Data consisting of numbers that measure or count something, usually with units.
- Identification Number — A number used only to identify, not to measure or count; considered categorical.
Action Items / Next Steps
- Practice identifying whether sample data sets are categorical or quantitative.
- Review class notes on examples of both data types.