Simple Random Sampling: Definition and Method Guide

If you are an organization with thousands of customers, surveying your entire customer base can seem like a daunting task. The likelihood of all of your customers (whether they number in the thousands or in the hundreds) answering a survey is slim to none. But, customer feedback is critical to driving Experience Improvement and growing your CX program. So, with such slim odds, how are you supposed to trust the customer feedback you do get? 

Simple random sampling is the perfect solution to this problem. Sampling methods such as simple random sampling allow businesses to get the data they need to make decisions without having to go through any unnecessary work. The goal of using simple random sampling is to get a small group that is unbiased representative of a larger population.

What is Simple Random Sampling and Why Is It Important?

A simple random sample is a selection of participants from a population. What makes this sampling method different is that each participant has an equal probability of being selected.   

Simple random sampling is important for many reasons. First, the subgroups from your population will be unbiased. Since they were chosen at random, they do not have any predisposed bias as participants, and they do not suffer from researcher bias. 

It is also important because these unbiased groups allow businesses to get data that is reflective of the entire population, without actually having to survey the entire population. 

What Are the Advantages and Disadvantages of Simple Random Sampling?

Many businesses prefer simple random sampling for its simplicity and lack of bias. It is also the easiest form of sampling. You don’t need to be a data analyst in order to perform this sampling method; and the data you receive can be applied to the whole population. 

The biggest disadvantage to be aware of is researcher bias. Researcher bias occurs when the researcher conducting the sampling selects participants to be in a subgroup based on their personal biases. This can be easily avoided by including multiple forms of random selection in your sampling. 

How Do You Perform Simple Random Sampling?

Simple random sampling is the perfect tool for a company looking to get an idea of how its entire customer base feels about a certain subject. Let’s say that you work for a nationwide retailer, and are interested in finding out how your loyalty members feel about a new selection of loyalty perks that you are considering to develop. 

You could go into the database that has a list of all of your loyalty members or, in this case, the list of your population. After assigning each member a number, you could use a random number generator to randomly select the number of participants you have chosen for your sample size. 

This subgroup of members, chosen at random, can now be surveyed. Their responses can be analyzed and can also be viewed as being representative of your entire member base. 

Sampling With InMoment

If you’re interested in understanding your customer base without having to survey each and every customer, then simple random sampling may be your answer. But understanding that data, what it means, and what to do with it moving forward can be difficult. 

InMoment, the leader in people-oriented text analytics, can help. Built on industry-recognized metrics and real-time intelligence, InMoment provides the tools and support you need to find hidden insights in your data. For more information on data gathering and analysis, visit our Learning Hub and look at how our data studios can be beneficial to your business. 

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