# What is random sampling? What are its merits and demerits?

Random sampling are the most effective way to choose a sample from a population of interest. The benefits are that your sample should represent the target population and reduce sampling bias; yet, it is extremely difficult to achieve (i.e., time, effort, and money). Random sampling is commonly used in scientific studies, opinion polls, and census operations.

There are two types of random samples: simple and systematic. In simple random sampling, each member of the population has an equal chance of being selected for the sample. This type of sampling is easy to implement but can be inefficient use of resources because not every member of the population needs to be considered. For example, if you were selecting students for a survey, you would not want to select all seniors or all freshmen. Instead, you would want to select members of the population at random. Simple random sampling can also result in a biased sample if, for example, those who are sick are more likely to be excluded from the study. Systematic random sampling begins with one element of the population and then repeats this step until the desired number of elements have been selected. For example, if you were selecting students for a survey, you could start with the first student listed on the roster and then repeat this step until you had selected the same number of students as there are seats available in your class. This method ensures that you include everyone in the population but may take longer than simple random sampling.

## What does "random selection" mean?

Random selection, often known as random sampling, is a method of selecting individuals of a population to be included in your study's sample. Random sampling increases your results' external validity or generalizability, whereas random assignment improves your study's internal validity. In other words, by choosing participants randomly, you are increasing the chances that the findings can be applied to the whole population.

There are two types of random selections: simple and systematic. With simple random selection, each unit (person or group) has an equal chance of being selected for inclusion in the study. For example, you could select every other person when going down the hallway at school. In this case, the selection process would not be considered random because people not chosen would be more likely to be female or older than those who were chosen. This type of selection tends to yield results that are statistically reliable but may not be representative of the entire population.

Systematic random selection uses criteria or rules to select each unit. For example, you could pick names out of a hat to choose participants for a study. In this case, the selection process would be considered random even though it was not entirely so. Names were used as the only criterion for selection and so some groups might have been over-represented and others under-represented compared with the overall population.

## What are the advantages of random sampling?

What Are the Benefits of Random Samples?

• It offers a chance to perform data analysis that has less risk of carrying an error.
• There is an equal chance of selection.
• It requires less knowledge to complete the research.
• It is the simplest form of data collection.

## What is the purpose of random sampling for the researcher?

Random sampling guarantees that the findings received from your sample are close to those obtained if the full population was surveyed (Shadish et al., 2002). The simplest random sample gives each unit in the population an equal probability of being chosen. Thus, a simple random sample consists of selecting members of the population without regard to their status as seen by the researcher or any other factor other than their being a member of the population. This method will produce a representative sample.

In practice, it is difficult to obtain a true random sample because not all groups have an equal chance of being selected for study. For example, if there is a high concentration of one type of person within the population, they are more likely to be selected for study. Simple random sampling can be improved upon by using a stratified sample, in which participants are selected according to characteristics that affect their likelihood of being included in the sample. For example, if age were a relevant characteristic, then younger people might be under-represented in the sample. Gender could also be used as a strata variable, with males and females being selected independently for study until the desired number of individuals from each group is represented in the sample.

It is important to realize that simple random sampling alone cannot guarantee representation of different groups within the population. If groups are unequally distributed among the sampled units, then these samples will not be representative of the whole population.

##### Jason Turner

Jason Turner is a military veteran and freelance writer. He enjoys working with words to make people think about their actions and inspire them to change their lives for the better. His goal is to create stories that will last hundreds of years; he hopes his work can be read by many generations of readers long after he's gone.

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