Statistics notes  types of data
From Helpful
This is more for overview of my own than for teaching or exercise.
Other data analysis, data summarization, learning

This article/section is a stub — probably a pile of halfsorted notes, is not wellchecked so may have incorrect bits. (Feel free to ignore, fix, or tell me) 
Contents
 1 One possible typology
 1.1 Ratio data (ordered, meaningful zero point, linear scale, (often) continuous)
 1.2 Interval data (ordered, no meaningful zero point, possible linear scale, (often) continuous)
 1.3 Discrete numeric data (ordered, linear scale, discrete)
 1.4 Ordinal data (ordered, but no obvious numbering so not linear; discrete)
 1.5 Nominal/categorical data (unordered; qualitative; discrete)
 2 More words
 3 Variables, dimensions, and measurement, and experiments
One possible typology
Ratio data (ordered, meaningful zero point, linear scale, (often) continuous)
Examples:
 weight
 length
 time amount  reaction time, hours of study, time required to run a marathon
 age
 temperature in Kelvin
 number of responses (overlap with discrete numeric)
Interval data (ordered, no meaningful zero point, possible linear scale, (often) continuous)
Discrete numeric data (ordered, linear scale, discrete)
Ordinal data (ordered, but no obvious numbering so not linear; discrete)
Examples:
 highest level of education
 questionnaire items of the disagree/disagree, happy/unhappy type
 socioeconomic status
 age groups ('age up to 20', 'age 2029', 'age 3039') (note: in this case based on ratio data)
 ranks
Asking people to rate on a fewpoint scale is often seen as ordinal, while there is overlap with continuous interval data.
Nominal/categorical data (unordered; qualitative; discrete)
Examples:
 labels, such as {T,A,G,C}
 choosing from multiple choice options
 distinguishers such as {green,blue} or {true,false}, (discrete) gender, blood type
 brand names
 left/righthandedness
 political affiliation
More words
More complex cases
Implications
Further terms that matter
Continuous data refers to valued numbering that is not restricted to be discrete/integer.
 so ratio or interval data in the above list.
Quantitative data  basically anything not categorical, so referring to nominal/categorical.