Advantages and disadvantages of random sampling

In this technique, each member of the population has the same probability of being selected as a subject. The whole process of sampling is done in one step, where each subject is selected independently of the other members of the population .

Random sampling can only be applied in many methods. The most primitive and mechanical would be the lottery. Each member of the population is assigned a number. All numbers are placed in a container or a hat and mixed. Blindfolded, the researcher takes out the labels with numbers. All individuals who have the numbers drawn by the researcher are the subjects of the study. Another way would be for a computer to randomly select the population. In the case of populations with few members, it is advisable to use the first method, but if the population has many members, a random selection by computer is preferable.

Advantages of simple random sampling

One of the best things about  simple random sampling is the ease of assembling the sample. It is also considered a fair way to select a sample from a population, since each member has equal opportunities to be selected.

Another key feature of simple random sampling is the representativeness of the population. In theory, the only thing that can jeopardize its representativeness is luck. If the sample is not representative of the population, the random variation is called  sampling error .

In order to  draw conclusions from the results of a study, an impartial random selection and a representative sample are important. Remember that one of the objectives of the research is to draw conclusions regarding the population from the results of a sample. Due to the representativeness of a sample obtained by simple random sampling, it is reasonable to make generalizations from the results of the sample with respect to the population.

Among its strengths are that it tends to produce representative samples and allows the use of inferential statistics in the analysis of collected data .

Each selection is independent of other selections; All possible combinations of sampling units have the same opportunity to be selected. In systematic sampling, the possibilities of being selected are not independent of each other.

In general, it is easier than other probabilistic sampling procedures (such as conglomerate sampling) to understand and communicate to others.

The statistical procedures required to analyze the data and calculate errors are easier than those required in other probabilistic sampling procedures.

Disadvantages of simple random sampling

One of the most obvious limitations of simple random sampling is the need for a complete list of all members of the population. You must bear in mind that the list of the population must be complete and updated. This list is generally not available in large populations. In these cases, it is more prudent to use other sampling techniques

Among the disadvantages are that a sampling frame of elements of the target population is required. An appropriate sampling frame may not exist for the target population, and it may not be feasible or practical to build one. In this case, the sampling by conglomerates does not require a sampling of the elements of the target population.

Simple random sampling tends to have larger sampling errors and less stratified sampling precision of the same sample size.

Respondents can be very dispersed, therefore, the costs of data collection may be higher than those of other probability sample designs, such as cluster sampling.

Simple random sampling may not produce a sufficient number of elements from small subgroups. This would not make simple random sampling a good option for studies that require a comparative analysis of the small categories of a population with much broader categories of the population.

Strengths and weaknesses of simple random sampling

Advantages Disadvantages
In comparison with other probabilistic sampling procedures: In comparison with other probabilistic sampling procedures
Each possible combination of sampling has an equal probability of being selected. It does not take advantage of the knowledge that the researcher could have of the population.
Easier to understand and communicate to others. You may have larger sampling errors and less accuracy than other probabilistic sampling designs with the same sample size.
It tends to produce representative samples. If subgroups of the population have particular interests they can not be included with a sufficient number in the sample.
The statistical procedures needed to analyze data errors and statistics software are easier. If the population is very dispersed, the costs for data collection may be higher than for other designs in the probability sample.


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