continued from previous message... In response to Fred Stutzman: I won't re-quote half of your response although it was all very useful to me. I would be most grateful for any text/reference you can provide that discusses the use of inferential statistics on a purposive sample - "to draw inference about relations in data". Again, maybe I am missing a piece of the puzzle, but why use inferential statistics to draw inferences about the relations between variables when we can just measure them - if we are assuming the sample is the population. Eg. I ask of my data, are monthly+ ecstasy users younger than occasional ecstasy users? and I find regular users are, for example, mean age 20 compared to mean age 25 for occasional users. Because I don't know the bias in my sample, I don't know if this will be wildly different with another sample of this population, so I could just report the two means and note the difference. Although it makes my research look more sophisticated with the p value (and even confidence intervals around those means), I'm not convinced that it makes sense to include them! In response to Peter Timusk: Sorry if that came across as absolute in my original post - of course there are examples of probability samples of drug users. These are generally the exception rather than the norm, though. And I would not seek to generalise to a wider population of drug users from my sample - I'm really just interested in understanding them as a population, knowing they would differ in substantive ways from more general populations of people who use drugs. As for the size of the sample, again, I'm concerned that the size of the sample is less of an issue than the way it is sampled. Having a larger sample with unknown bias is still a sample with unknown bias! Thanks again (and sorry for the rather long post) Monica