An issue that almost all quantitative researchers deal with has to do with missing data.

What is that?

Well, when we have defined our population and conducted a probability sampling, we start collecting data for the individuals in our study sample – either through questionnaires or registers (or both). It is very seldom the case, however, that we get complete information for all individuals. We thus get missing data.

When we use register data, missing data is commonly called attrition, and when we use survey data (i.e. questionnaire data), missing data is usually called non-response.

If we have problems with missing data, we run into problems with representativeness, which may prevent us to draw conclusions about the population based on the study sample. This is discussed in further detail in Missing data.  

Population (N)		Study sample (n) (with missing data)
	Sampling
	Representativeness