Simple Random Sampling
Definition:
Simple Random Sampling is a type of probability sampling method, where each member of the population has an equal and independent chance of being selected for the sample. This is the most basic and fundamental method of sampling and is used as a starting point for more complex methods. Simple random sampling can be used to select a representative sample from a large population in order to make inferences about that population.
How to conduct Simple Random Sampling
To conduct a simple random sampling, you will need to:
Define your population:
The first step is to identify the population from which you will be selecting your sample. This can be done by looking at records or lists of individuals who meet certain criteria (e.g. all students enrolled in a particular school).
Select a method for generating your sample:
There are several ways to generate a simple random sample, but the most common is to use a random number generator. This is a computer program that will randomly generate numbers from 0 to whatever the maximum value of the sample size.
Select your sample
Once you have generated random numbers (using a random number generator), use those numbers to select individuals from your population. For example, if you had a population of 6 and a sample size of 100, then use the first 6 digits from your random number generator to select each individual from your population (1-6).
Check that your sample is representative:
After you have selected the sample, check to make sure that your sample is representative of the population. For example, if you had a population of 6 people and a sample size of 100, and you randomly selected 1 person from this population, would it be representative?
When to use Simple Random Sampling
There are several situations when Simple Random Sampling should be used:
When the population is homogeneous:
If all members of the population are similar to each other, then Simple Random Sampling can be used to obtain a representative sample from that population.
When the researcher wants to study a small portion of the population:
If the researcher only wants to study a small portion of the population, then Simple Random Sampling can be used to select that portion of the population.
When the population is too large:
If the population is too large, then it would be extremely difficult and time-consuming to select every member of the population. Simple Random Sampling can be used to select a small portion of the population, which can then be studied in detail.
Example of Simple Random Sampling
Example:
If you wanted to select a random sample of 10 people from a population of 100, you would use simple random sampling.
To do this, you would first list all 100 names in the population. Then, you would use a random number generator to select 10 names from that list. This would be your sample.
Importance of Simple Random Sampling
The purpose of Simple Random Sampling is to ensure that all members of the population have an equal chance of being selected for the study. This type of sampling is important because it allows researchers to make inferences about a population based on a representative sample.
Advantages of Simple Random Sampling
There are several advantages of using simple random sampling when conducting research.
- It is easy to select a sample using this method. All that is needed is a list of all the units in the population and a way to randomly select them.
- This can be done by using a random number generator or by picking names out of a hat.
- It is easy to calculate the margin of error. This is because the calculations are based on the assumption that the population is homogeneous, which means that all units in the population are equally likely to be selected.
- Simple random sampling also has the advantage of being able to produce unbiased results. This is because each unit in the population has an equal chance of being selected, so there is no bias towards any particular unit.
Disadvantages of Simple Random Sampling
There are a few disadvantages of using Simple Random Sampling:
- It can be difficult to obtain a representative sample if the population is large and/or spread out geographically. This can lead to bias in the results.
- Simple Random Sampling can be time-consuming and expensive, particularly if the researcher needs to contact each potential participant individually.
- There is always the possibility of human error when conducting this type of research, which could impact the validity of the results.
