**Under what kind of conditions would you recommend. **

**i) A probability sample? A non probability sample?**

# ii) A simple random sample? A clusser sample? A stratified sample?

Any discussion of the relative merits of probability versus nonprobability sampling clearly shows the technical superiority of the former. In __probability sampling__, researchers use a random selection of elements to reduce or eliminate sampling bias. Under such conditions, we can have substantial confidence that the sample is representative of the population from which it is drawn. In addition, with probability sample designs, we can estimate an interval range within which the population parameter is expected to fall. Thus, we not only can reduce the chance for sampling error but also can estimate the range of probable sampling error present.

With a subjective approach like __nonprobability sampling__, the probability of selecting population elements in unknown. There are a variety of ways to choose persons or cases to include in the sample. Often we allow the choice of subjects to be made by field workers on the scene. Under such conditions, there is greater opportunity for bias to enter the sample selection procedure and to distort the findings of the study. Also, we cannot estimate any range within which to expect the population parameter. Given the technical advantages of probability sampling over nonprobability sampling, why would anyone choose the latter? These are some practical reasons for using these less precise methods.

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