Simple Random Sampling: Definition and Examples

A simple random sampling is. a technique to give members an equal chance of survey participation. Choose the right audience for surveys.

Simple random sampling is a statistical method in which everyone in a population has an equal chance of being selected into a sample. The sample represents a smaller and more manageable portion of the people that can be studied and analyzed. It’s a fundamental technique to gather data and make inferences about a population.

Simple random sampling is considered a fair and unbiased sample selection method. This type of sampling is the most straightforward sample selection bias method.

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What is Simple Random Sampling?

Simple random sampling is a technique where every item in the population has an even chance and likelihood of being selected. Here, the selection of items entirely depends on luck or probability. Therefore, this sampling technique is also a method of chance.

Simple random sampling is a fundamental method and can easily be a component of a more complex method. The main attribute of this sampling method is that every sample has the same probability of being chosen.

The sample size in a simple random sampling method should ideally be more than a few hundred so that it can be applied appropriately. This method is theoretically simple to understand but difficult to implement practically. Working with a large sample size isn’t an easy task, and it can sometimes be challenging to find a realistic sampling bias frame.

Simple Random Sampling Methods

Researchers follow these methods to select a simple random sample:

  1. They prepare a list of all the population members initially, and each member is marked with a specific number ( for example, if there are nth members, then they will be numbered from 1 to N).
  2. Researchers from this population choose random samples using random number tables and random number generator software. Researchers prefer random number generator software, as no human interference is necessary to generate samples.

Two approaches aim to minimize any biases in the process of this method:

01. Method of lottery

Using the lottery method is one of the oldest ways and is a mechanical example of a random sample. Researchers draw numbers from the box randomly to choose samples. In this method, the researcher gives each member of the population a number.

02. Use of random numbers

Using random numbers is an alternative method that also involves numbering the population. A numbered table similar to the one below can help with this sampling technique.

simple random sampling

Simple Random Sampling Formula

Consider that a hospital has 1000 staff members and must allocate a night shift to 100 members. All their names will be put in a bucket to be randomly selected. Since each person has an equal chance of being selected. Since we know the population size (N) and sample size (n), the calculation can be as follows:

Simple Random Sampling Steps

Simple random sampling is a crucial method in statistical analysis for drawing unbiased conclusions about a population. Below are the steps to perform simple random sampling to select a sample of 100 employees out of a total of 500 in an organization.

simple random sampling

Step 1: Make a List

To start simple random sampling, first, make a complete list of all 500 employees in the organization. It’s important that the list includes the names of every employee to guarantee that each person is considered.

A precise and thorough list is crucial to ensure the sampling accurately reflects the entire population.

Step 2: Assign a Sequential Number

After creating the list of employees, the next thing to do is give each employee a number in order. This is your sampling frame (the list from which you draw your sample). This numbering helps organize the list, making identifying each person in the group easier.

Every employee should have their own number, starting from 1 and going up to n, which is the total number of employees in the organization.

Step 3: Choose Sample Size

Selecting the right sample size is important in simple random sampling. In this situation, we’ve chosen a sample of 100 employees from a total population of 500. It’s essential to pick a sample size that’s large enough for dependable results but still practical for analysis.

Step 4: Use a Random Number Generator

To choose a sample from the group, use a random number generator. First, find the total number of people (Step 2) and decide how many we want in our sample (Step 3).

Then, use a random number table or generator to create 100 different random numbers between 1 and 500. These numbers match the order given to each employee, which helps you pick who will be in the sample.

This method ensures that each employee has an equal opportunity for selection, maintaining fairness and impartiality in sample selection.

It is important to note that Simple Random Sampling is just one of many sampling methods available, and it may not always be the best option for your specific research needs.

Simple Random Sample vs Other Sampling Methods

When thinking about how to sample, people often look at different methods like simple random sampling, stratified sampling, systematic sampling, and cluster sampling. Each method has its pros and cons, so it’s crucial to choose the right one depending on what you’re studying and the features of the group you’re looking at.

Simple vs Stratified Random Sample

The simple random sampling techniques and stratified random sampling have different ways of choosing samples from a population.