Simple random sampling is considered the easiest and most popular method of probability sampling. To perform simple random sampling, all a researcher must do is ensure that all members of the population are included in a master list, and that subjects are then selected randomly from this master list.

While simple random sampling creates samples that are highly representative of the population, it can be time consuming and tedious when creating large samples.

### Steps of Simple random sampling

The following 8-step procedure may be followed in drawing a simple random sample of n units from a population of N units.
•     Assign serial numbers to the units in the population from 1 through N.
•      Decide on the random number table to be used.
•      Choose an N-digit random number from any point in the random number table.
•      If this random number is less than or equal to N, this is your first selected unit.
•      Move on to the next random number not exceeding N, vertically, horizontally or in any other direction systematically and choose your second unit.
•      If at any stage of your selection, the random number chosen exceeds N, discard it and choose the next random number.
•      If, further, any random number is repeated, it must also be discarded and be replaced by a fresh random number appearing next.
•      The process stops once you arrive at your desired sample size.
There are two approaches that aim to minimize any biases in the process of simple random sampling:
• Methods of lottery
• Random number table method
Method of lottery
Using the method of the lottery is one of the oldest methods and is a mechanical example of random sampling. In this method, each member of the population has to number systematically and in a consequent manner by writing each number on a separate piece of paper. These pieces of paper are mixed and put into a box and then numbers are drawn out of the box in a random manner.

Random number table
The use of random numbers is an alternative method that also involves numbering the population. The use of a number table similar to the one below can help with this sampling technique.

Applications of simple random sampling
•      A list of all members of population is prepared. Each element is marked with a specific number (suppose from 1 to N).
•      items are chosen among a population size of N. This can be done either with the use of random number tables or random number generator software.
•      The Aromatic Company is planning to conduct a study to estimate the proportion of toilet soap users who prefer a certain color or flavor of their product. A simple random sample of customers may be used for this purpose. It is assumed in this case that a list (sampling frame) of the consumers is available to the research team.
•      A forester in Chittagong Hill Tracts may wish to estimate the volume of timber or proportion of diseased trees in a forest by se geographic points in the area covered by the forest and then attaching a plot of fixed size and shape to that point. All the trees within the sample plots may be studied. But again the basic design is a simple random sample.

• It is a fair method of sampling and if applied appropriately it helps to reduce any bias involved as compared to any other sampling method involved.
• Since it involves a large sample frame it is usually easy to pick smaller sample size from the existing larger population.
• The person who is conducting the research doesn’t need to have a prior knowledge of the data being collected. One can simply ask a question to gather the researcher need not be a subject expert.
• This sampling method is a very basic method of collecting the data. There is no technical knowledge required and need basic listening and recording skills.
• Since the population size is large in this type of sampling method there is no restriction on the sample size that needs to be created. From a larger population, you can get a small sample quite easily.
• The data collected through this sampling method is well informed, more the samples better is the quality of the data.
• It is a costlier method of sampling as it requires a complete list of all potential respondents to be available beforehand.
• This sampling method is not suitable for studies involving face-to-face interviews as covering large geographical areas have cost and time constraints.
• A sample size that is too large is also problematic since every member of the population has an equal chance of selection. The larger population means a larger sample frame. It is difficult to manage the large population.
• The quality of the data depends on the researcher and his/her perspective. If the researcher is experienced then there are fair chances the quality of data collected is of a superior quality. But if the researcher is inexperienced then the data collected may or may not be up to the mark.