Types of Data

Statistics · Axiom Academy

Understanding how we classify and measure data in statistics 1 The Two Main Types: Categorical vs Quantitative Categorical: Describes qualities or characteristics; cannot perform arithmetic operations Quantitative: Numerical values; can perform mathematical calculations Visual cues: If you can meaningfully average values, it's likely quantitative 2 Quantitative Data: Discrete vs Continuous While continuous data theoretically has infinite precision, in practice we round measurements (e.g., height to nearest cm). This doesn't change its continuous nature—it's still measured, not counted. 3 Categorical Scales: Nominal and Ordinal Nominal: Categories with no order; only mode makes sense as central tendency Ordinal: Categories with meaningful order; differences between ranks may not be equal Test: Ask "Can these be ranked meaningfully?" If yes, it's ordinal; if no, it's nominal 4 Quantitative Scales: Interval and Ratio Can you say "twice as much"? If 20 kg is twice as heavy as 10 kg (ratio scale), that makes sense. But is 20°C twice as hot as 10°C? Not really—that's why Celsius is interval, not ratio. In Kelvin, with true zero, you could make this comparison. 5 The Complete Data Classification Framework Nominal: Use bar charts, pie charts; calculate mode and frequencies Ordinal: Use bar charts, calculate median and mode; be cautious with mean Interval: Use histograms, calculate mean and standard deviation; differences are meaningful

This is the written version of the interactive lesson above. See the full Statistics course.