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Simple Random Sampling : Definition, Method & Examples

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What is Simple Random Sampling?

Simple random sampling is a sampling method used in market research studies that falls under the category of probability sampling. This means that when employed, simple random sampling gives everyone in the target population an equal and known probability of being selected as a respondent in the sample group.

Simple random sampling assigns numbers to everyone within the population, so that a sample group may be selected using processes that pick random numbers from the list.

This method of sampling is most apt for when the main objective of a study is for its findings to be generalizable for a whole population. In other words, this method of sampling ensures that the data extracted from the chosen sample group is reflective of what it would be for the target population as a whole.

Simple random sampling is usually used for large populations, hence, it is important to ensure a sample size that is large enough to fittingly represent this population.

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Methods of Simple Random Sampling

In order to use simple random sampling in a market research study, a researcher must know the size of the population in order to ascertain the number of total units in the population. After finding the total number of units/people within the population, the researcher must then assign serial numbers to each one of them.

For example, if the study is on the employees of an organization that has 300 employees, each one of them must be assigned a number as they are the population from which the sample must be drawn.

After the numbers have been assigned, there are a few common ways in which the required sample size can be drawn from the population:

  1. Lottery method: This method involves all the serial numbers being written down on chits of paper and being put into a container that is properly mixed manually. Then, chits are picked out of the container to select the sample group.
  2. Software packages: Different market research software packages are used by researchers to pick a sample group. Excel and SPSS are the most common. Necessary commands must be input in regard to population size and required sample size.
  3. Random number tables: Random number tables, such as the one below ranging from 000-300, can also be used. After creating a random number table with the serial numbers of your target population, you may select specific columns/rows for your sample group according to your decided sample size.
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When can Simple Random Sampling be used?

  1. When you have the time, resources, an omnichannel market research tool, and financial means/funding to carry it out.
  2. When there is access to a complete list of everyone in the target population.
  3. When there is a way to contact/access everyone who is selected as a respondent in the sample group.

Advantages of Simple Random Sampling

  1. The findings are highly generalizable for the whole target population.
  2. It is easily understood and the results are highly projectable. In contrast to other sampling methods, this method doesn’t require additional steps such as breaking down the population into sub-populations. 
  3. Relative to other methods of sampling, simple random sampling has low bias. It fully eliminates human bias as samples are selected using random selection.
  4. The data collected through simple random sampling tends to be well informed and holistic.

Disadvantages of Simple Random Sampling

  1. It may not be as efficient as other sampling methods, such as stratified sampling.
  2. The potential for sampling error may exist as the use of ‘random selection’ may lead to the sample group not being reflective of the population. This is especially the case when the sample set of the target population is already not inclusive enough. Therefore, there is no assurance of “representativeness”. 
  3. It is not a favourable method of sampling in cases where the target population is very large and/or is widely geographically dispersed.

This method of sampling is also quite expensive, therefore in cases where cost is a primary consideration due to limited resources or funding, this isn’t a feasible sampling method.

Simple Random Sampling Versus Stratified Random Sampling

Stratified sampling, also a form of probability sampling, is where populations are broken down into subgroups or subsets based on certain criteria and samples are picked from each one of subgroups.

This, in contrast to simple random sampling, ensures that all subgroups in a population are appropriately represented in the sample group. However, this does make it a more complicated and tedious sampling method in comparison and researchers must make sure there is no overlapping of different subgroups/stratas.

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