Random Sampling

What Is Random Sampling?

This article will explain what random sampling is, its effectivity, and why it is still being used. If you are interested in random sampling and learning more about it then you should read on.

Random Sampling Defined

A survey is a very effective tool for gathering data from a group of people. When the group to be studied is very large, like the population of a large city or of a country, then a survey of the entire population is impossible to carry out. In situations like that, random sampling is used.

Researchers resort to sampling to derive data about a population by dealing with only a subset of that population. For sampling to work, the subset chosen for the study must be representative of the actual population.

There are two main categories for sampling. The first one is probability sampling which is more commonly known as random sampling and non-probability sampling. Under random sampling, every member of the population has an equal chance of being picked to participate in the survey.

A good illustration of this would be a lottery, where the population would be everyone who bought a ticket, assuming that each person is only allowed one ticket. In that case, everyone has an equal chance of being picked as the winner.

Under random sampling, a method for naming or numbering the members of a population must be defined. Once that is done, a method will be used for doing some kind of raffle to determine who would be picked. Under this method, it is possible to remove sampling bias, although it is not easy to achieve that completely.

Random Sampling Techniques

The trickiest problem faced by researchers using random sampling is ensuring the randomness of the participants. That can be the only way to ensure that they are representative of the entire population. 

There are several random sampling techniques in use right now. Here are the main ones:

Simple Random Sampling

As its name suggests, this is the simplest and most direct random sampling technique in use. Under this technique, the first step is to choose a sample size. Once that is done, the next step is to gather observations from the members of the population. Once the set sample size has been reached then the gathering of observation is concluded. 

Stratified Random Sampling

Under this technique, the members of a population must be divided into segments. Those segments are called strata. For example, the students who are members of a class can be divided into four groups:

  • Girls who achieved Grade A
  • Girls who did not achieve Grade A
  • Boys who achieved Grade A
  • Boys who did not achieve Grade A

Let’s say that you would like to achieve a sample size of 12, then you need to choose 3 persons from each subgroup.

When it comes to stratified random sampling, it’ s crucial that to have detailed information about the population to be able to create subgroups. In the example that we used, the grades and genders of the students are used in creating the strata. 

Cluster Sampling

Cluster sampling is similar to stratified sampling in one key point. It also divides the population into groups. Under this technique, the groups are known as clusters. Those clusters can be further subdivided if needed. 

A cluster can then be taken to represent the whole. This technique is normally used if the budget for the research is limited. It is more affordable since it can limit the area which will be covered by the researchers. The problem with just picking a cluster is that it’s not very random. 

Multi-Stage Sampling

This method is a kind of combination of the ones mentioned before. A population is divided into clusters and those clusters are further divided into-subclusters. This process is repeated until the clusters cannot be divided anymore. 

Convenience Sampling

There are cases when randomness is not the most crucial aspect of a survey. Sometimes speed and convenience are more important. During those cases, convenience is way important so researchers just base everything on who is available. 

Pros and Cons Of Random Sampling

Like any other method, random sampling has its pros and cons. Here are some of the advantages of using this technique:

  1. The main advantage of this technique is that it minimizes the possibility of bias. The participants are chosen at random, and that minimizes the possibility of bias.
  2. Random sampling is very simple and easy to implement. On top of that, it is considered to be a fair way of getting samples.
  3. It is very representative of the population. If done correctly, the only thing that can negatively impact the representativeness of random sampling is luck.
  4. Random sampling makes it reasonable to make generalizations regarding the population from the data gathered from it.

Now, here are the disadvantages of random sampling which you should be aware of:

  1. It is not easy to access the full list of a population, which is something that you will need to perform random sampling. Gaining access to the data that you may need to conduct random sampling may not be easy.
  2. Random sampling can be time-consuming. When the data of the population that is needed to conduct random sampling is unavailable, researchers must get the data from other sources. That can add to the overall period of the research.
  3. Aside from being time-consuming, getting the needed data can also be costly. Getting a full list of a population from a third party source can be costly but maybe a necessary cost.
  4. Finally, sample bias can still occur. This may happen when a subset is not inclusive enough. So, the persons conducting the research must be very familiar with the techniques to minimize the possibility of bias.

Why Random Sampling Is So important

Claiming that random sampling is absolutely representative of a population is not really true. As mentioned before, even if all bias is removed, luck can still play a part in the results. For example, all individuals are chosen for a survey can be, by a stroke of luck, inclined to answer no to the survey question.

The importance of random sampling is that it eliminates systematic bias because it gives all members of the population an equal chance of being picked. 

Random Sampling Example

Let’s say that a company is conducting research about its employees. The company has a total of 500 employees. Instead of going to each employee one by one, they choose to do random sampling. 

The researchers choose 50 employees out of the total of 500. They can do this by having a lottery out of the names of the employees from their list. They can assign a number to each employee and then they will pick 50. Once they have made their pick, they interview the chosen employees. The results from that can be taken to apply to the whole employee population.

That can be assumed since all employees had the same chances of being picked. At least in theory. There should always be room for error when using random sampling. 

Random sampling is still the best way of determining information about a large population using a small subset. It’s not a perfect method and it isn’t always right but if done correctly, it can offer some interesting insights into a population.

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