What is Sampling?
Since it is infeasible to examine an entire population, researchers frequently rely on sampling to obtain a subset of the population to conduct an experiment or observational study.
It is essential that the group chosen be representative of the population and not systematically prejudiced. For instance, a group composed of the wealthiest persons in a specific region would likely not truly represent the views of the entire population in that region. Consequently, randomisation is often used to obtain an unbiased sample.
Simple random sampling, stratified random sampling, and multistage random sampling are the most frequent sample designs. In this post, we will learn about simple random sampling.
Simple Random Sampling
The population is sampled randomly, using either a random number generator or a random number table, so that each individual in the population has an equal chance of being selected for the sample.
Simple random sampling is the most fundamental sampling approach, in which a set of participants (a sample) is selected for research from a wider population (a population). Each person is selected fully at random, and each member of the population has an equal chance of being included in the sample.
The objective of Simple Random Sampling
SRS is a random sampling technique. The objective of SRS is to sample a finite number of units randomly from a population according to a sampling scheme (a simple uniform or multistage scheme)
The method begins with the selection of the area or the population. Then a sampling frame is used to select a sample. In its simplest form, households are selected from the sampling frame, and each household is assigned a weight according to the number of people residing there. The individual weights are added up, and the final weight is used to represent the original population. The final weighted sample is the representative sample from which you draw your inference.
Equivalently, one may make a sequence of independent selections from the whole population, each unit having an equal probability of selection at each step, discarding repeat selections and continuing until n distinct units are obtained.
A simple random sample of n = 40 units from a population of N = 400 units is depicted in the Figure below:
Designs other than simple random sampling may give each unit an equal probability of being included in the sample. But with simple random sampling, each possible sample of units has the same probability.
SRS will generate a representative sample of the population. The procedure will not favour one subgroup over another. SRS will not choose a representative sample from this population. There is no systematic individual selection. The sample selection is random, implying that there is no control over who is chosen. All people have an equal chance of getting picked for a sample. This is significant since SRS does not make hiring decisions based on demographic factors such as age or race.
Benefits and Limitations of Simple Random Sampling
Benefits of Simple Random Sampling
Among the advantages of simple random sampling is the simplicity of sample assembly. It is also regarded as a fair method for selecting a sample from a particular population because each member has an equal chance of being chosen.
The representativeness of a simple random sample is another important characteristic. Luck is the only theoretical factor that could damage its representativeness. The random variance if the sample is not representative of the population is known as sampling error.
Conclusions drawn from a study’s results must be based on an impartial random selection and a representative sample. Remember that one of the purposes of research is to draw population-level inferences from the results of a sample. Due to the representativeness of a sample acquired through simple random sampling, it is feasible to extrapolate the sample’s findings to the entire population.
Limitations of Simple Random Sampling
The necessity for a comprehensive list of all members of the population is one of the most evident drawbacks of the basic random sampling approach. Please bear in mind that the population list must be comprehensive and current. Typically, this list is unavailable for big populations. In such situations, it is prudent to employ other sampling methods.
Simple Random Sampling is the most basic sampling method. If you have ever utilised a random number generator, you have performed SRS. Random sampling is also known as a ‘simple random sample’ or’simple random selection,’ and it is used whenever objects from a population must be sampled. For instance, while attempting to determine how many members of a specific kind (group) of items are present in a group of objects.