Discrete data is information that can be categorized into a classification. Discrete data is based on counts. Only a finite number of values is possible, and the values cannot be subdivided meaningfully. For example, the number of parts damaged in shipment.
Attribute data (aka Discrete data) is data that can’t be broken down into a smaller unit and add additional meaning. It is typically things counted in whole numbers. There is no such thing as ‘half a defect.’ Population data is attribute because you are generally counting people and putting them into various catagories (i.e. you are counting their ‘attributes’). I know, you were about to ask about the ‘2.4 kids’ statistic when they talk about average house holds. But that actually illustrates my point. Who ever heard of .4 of a person. It doesn’t really add addition ‘meaning’ to the description.
See Continuous Data for alternative data type.
Observations made by categorizing subjects so that there is a distinct interval between any two possible values. “Good or Bad” and “Tall or Short”