A simple random sample is a subset of population chosen in such a way that all members of the population have an equal chance of being selected. It is the most basic form of sampling and is often used in research studies, surveys and other types of data collection. The ability to choose a simple random sample allows researchers to make sure that the data they collect is representative of the population as a whole. This type of sampling allows researchers to draw inferences and make generalizations about the entire population. In this blog post, we will discuss what a simple random sample is, provide examples of how it is used and explain why it is important. We will also discuss the advantages and disadvantages of this type of sampling. With this information, readers will gain a better understanding of simple random sampling and be able to decide if this is the best sampling method for their research.
Simple Random Sampling
How to use a simple random sample
To use a simple random sample, follow these steps:
1. Determine your population
Before you can take a simple random sample, you must know who you want to sample. Therefore, you must include a hypothesis in your research project or experiment that can be answered by examining a sample. Let’s say, as an example, that you want to research the personality characteristics of local politicians. You must first identify “local politicians” for your research. These could be representatives from the state, the city, the county, or all three at once.
2. Make a sampling frame
Once your population has been established, you must create a sampling frame. A list of every person in your population serves as your sampling frame.
Once you’ve determined the population, it should be fairly simple for local government officials to obtain the names of your state and local officials. This is an ideal situation. It might be more difficult in some circumstances to locate every member of your population for your sampling frame. However, if you decide to only include a portion of the population in your sampling frame, you could expose your research to unintended bias. As a result, it is advised that you create your sampling frame carefully.
After listing every member of your population, you must sequentially assign numbers to each asset. This is crucial for creating the sampling frame because it allows you to choose randomly. Imagine that there are 100 local politicians in your community. Each one must be given a number between 1 and 100.
3. Determine sample size
Next, you will decide the size of your sample. Take into account one of the following methods when calculating your sample size:
4. Generate random numbers
Create a range of random numbers between 1 and the size of your entire population that corresponds to your sample size using a random number generator. For instance, if your sample size is 10, and your population is 100 politicians, you will need to create 10 random numbers between 1 and 100.
5. Isolate your selection
Each number you produce, in a manner similar to a lottery, will represent a participant in your sample who was given a number in step two. Once your choice has been determined and isolated, you have taken a random sample.
What is a simple random sample?
A simple random sample is a term used to describe a statistical population’s selection made at random. It should offer an unbiased representation of the larger group. Random sampling is more effective than other sampling techniques in that it is the quickest way to select a sample from a larger group. It’s a fundamental place to start when gathering a sample, and some other sampling techniques progress from a straightforward random sample to more intricate ones.
In practice, the premise behind simple random sampling is probability. There is an equal chance that each selection will be chosen for the sample. Without replacement, as is frequently the case, simple random sampling means that the selected individual cannot be drawn again from the population. A simple random sample must have a full selection frame to choose from in order to be performed properly. It may be difficult to obtain a selection frame, which is a list of the entire population, in larger samples.
Advantages of simple random samples
Applying a straightforward random sample to obtain a sample of your population has some benefits, such as:
Disadvantages of simple random samples
Sampling error is the main drawback of using simple random sampling. Even though the sample was chosen at random and without bias, this happens when it doesn’t accurately represent the population.
A sample error would occur, for instance, if only men were included in your random selection of ten politicians from a population of 100 politicians who are equally split between men and women. Because of this, simple random sampling is typically only employed when little information about the population as a whole is available. Other sampling techniques might be more appropriate as more parameters may need to be set for the sample as you learn more about the population.
Other types of random samples
There are additional ways to choose populations at random, such as:
An adaptive design can be used with a simple random sample to create and modify samples based on new traits.
