Sampling Techniques A sampling procedure that ensures that every element of the population has the same probability of being selected is called simple random sampling. Suppose we have a school with 1000 students, divided equally into boys and girls. girls, and you wanted to select 100 of them for further study. You could put all their names in a drum and then come up with 100 names. Not only does each person have the same probability of being selected, but we can also easily calculate the probability of a given person being chosen, since we know the sample size (n) and the population (N) and it becomes a simple matter of division: n / N x 100 or 100/1000 x 100 = 10%Systematic samplingAt first glance it is very different. Suppose that the N population units are numbered from 1 to N in some order. To select a systematic sample of n units, if $k approximately N/n$then every kth unit is selected starting with a randomly chosen number between 1 and k. So selecting the first unit determines the entire sample, for example N = 5,000, n = 250 then k = 5,000/250 = 20. Therefore, select every 20th element starting (for example) with 6. Question: Is this equivalent to sampling simple random? Strictly speaking, the answer is No!, unless the list itself is in random order, which it never is (alphabetical, seniority, house number, etc.). Advantage(s) easier to extract, without errors (cards in tab)(ii)More accurate than simple random sampling because overpopulation is more evenly distributed. Disadvantages (i) If the list has a periodic arrangement, it can go very wrong. Stratified Sampling In this random sampling technique, the entire population is first divided into mutually exclusive subgroups or strata and then units are randomly selected from each stratum. Segments are based on some predetermined criteria such as geographic location, size or demographic characteristics. It is important that the segments are as heterogeneous as possible.