Variable Types Decision Tree
This interactive decision tree helps students classify any variable they encounter into its proper type: Continuous, Discrete, Ordinal, or Nominal.
Run the Variable Types Decision Tree in Full Screen
How to Use This Diagram
- Start by examining the variable you want to classify
- Ask yourself the key questions as you follow the flowchart
- Hover over any node to see additional examples and explanations
- Click "Test Yourself" to practice classifying random variables
The Two Main Categories
Numerical Data (Can do math with it)
Values that represent quantities or measurements where mathematical operations make sense.
- Continuous: Can take any value within a range, including decimals
- Examples: Height (5.7 ft), Temperature (98.6F), Time (3.5 hours)
- Discrete: Can only take specific, countable values (usually whole numbers)
- Examples: Number of siblings (2), Cars owned (3), Students in class (25)
Categorical Data (Cannot do math with it)
Values that represent groups or categories where math operations are meaningless.
- Ordinal: Categories with a meaningful order or ranking
- Examples: Grade levels (Freshman < Senior), Satisfaction ratings (Poor < Excellent)
- Nominal: Categories with no inherent order
- Examples: Eye color (blue, brown, green), Blood type (A, B, AB, O)
Key Decision Questions
| Question | YES means... | NO means... |
|---|---|---|
| Can you do math with it? | Numerical data | Categorical data |
| Can it be any value (including decimals)? | Continuous | Discrete |
| Is there a meaningful order? | Ordinal | Nominal |
Common Mistakes to Avoid
- ZIP codes are NOT numerical - Even though they contain digits, you can't meaningfully add or average them
- Age in years can be continuous - While often recorded as whole numbers, age is actually continuous (you can be 25.5 years old)
- Rating scales can be ordinal OR discrete - A 1-10 pain scale is ordinal (the order matters), but if you're counting how many times something happened, it's discrete