There are different sampling methods. We describe below some important types of sampling.

*(a) ***Simple random sampling.** In this type of sampling every unit of the population has an equal chance of being selected in a sample. There are two ways of drawing a simple random sample-With Replacement (WR) and Without Replacement (WOR).

In WR type, the drawn unit of the population is again returned to the population so that the size of the population remains same before each drawing. In WOR type, the drawn unit of the population LS not returned to the population. For finite population the size diminishes as the sampling process continues.

*(b) ***Systematic sampling.** In systematic sampling one unit is chosen at random from the population and the items are selected regularly at predetermined intervals. This method is quite good over the simple random sampling provided there is no deliberate attempt to change the sequence of the units in the population.

**(c) Cluster sampling.** When the population consists of certain group of clusters of units, may be advantageous and economical to select a few clusters of units and then examine all the units in the selected clusters. For example of certain goods which are packed in cartons and repacking is costly it is advisable to select only few cartons and inspect all the inside goods.

*(d) ***Two-stage sampling.** When the population consists of larger number of groups each consisting of a number of items, it may not be economical to select few groups and inspect all the items in the groups. In this case, the sample is selected in two stages. In the first stage, a desired number of groups (primary units) are selected at random and in the second stage, the required number of items are chosen at random from the selected primary units.

**(e) Stratified sampling.** Here the population is subdivided into several parts, called strata showing the heterogenity of the items is not so prominent and then a sub sample is selected from each of the strata. All the sub-samples combined together give the stratified sample. This sampling is useful when the population is heterogeneous.