# Is it necessary for a sample to broadly reflect the target population distribution?

Following the question here, the target population is made up of 600 units which are distributed as follows:

• while units (17%)
• black units (42%)
• green units (8%)
• yellow units (33%)

A representative sample would be one which broadly reflects this percentage distribution.

However, in this case, one would end up with only a small number of green units (because of their lowest population distribution) thus it is not reliable to analyse the green units on this own. For example, in a sample of 100 units, about 8 will be green units

I want to analyse each colour units on their own (as well as overall) and I understand one way to overcome the above issue is to oversample the green units.

Question: What is the risk of oversampling the green units but keeping the number of other units fixed?

For example, in a sample of 100, I can have approximately 25 units of each colour.

This is out of practical consideration such as the cost of data collection.

• Can you explain what you mean by "risk" of oversampling? Oversampling is an accepted method for enabling accurate estimates to be made for rarer populations, see for example statcan.gc.ca/pub/12-001-x/2009002/article/11036-eng.pdf What do you want to do, so you can get advice specific to your situation? – Michelle Jan 25 '12 at 3:02
• I guess "risk" is not the right word here. I mean something to the effect whether "over-representing the green units while keeping the number of other units constant to accommodate this overrepresentation" is a good way of sampling. By keeping the number of other units constant to accommodate the over-represention, I would be inadvertently under-representing them. – Adhesh Josh Jan 25 '12 at 4:11