When you want to be certain that you’re sample is representative for the population you’ve studied you can use a technique or procedure called statistical weighting. If you’re looking for a representative sample, it has to be of the same composition as the population that you’re studying.
Example: imagine a population where 40% of the people are above 40 years of age and 60% are below 40 years of age. If your sample has a composition of 60% above 40 years and 40% below, it isn’t representative for the population.
However, age might not be all that relevant as a variable, when you’re looking for accurate results. Variables like having experienced the product or service have a much larger influence on the attitudes and behaviour that your population displays towards said product or survey.
Standard demographics like age and gender might very well not be all that interesting. Still, many researchers default to statistics like that regularly and far too easily when weighting a certain data set or applying statistical weights.
Think carefully when you’re considering which variables you’ll use when applying statistical weighting. Picking the ‘wrong’ variable could give you results that aren’t representative for your project at all.
