What Is Sampling Bias And How Do You Avoid It?

What Is Sampling Bias And How Do You Avoid It?

Key Findings:

  • Sampling bias occurs when some members of the intended population have a higher or lower probability of being selected than others as a result of how the data were collected.
  • One way to avoid sample bias is to ask the right questions in your surveys.
  • Establish an accurate sample size and examine the population that you identified for your research with stratified random sampling.

It's a well-known fact that sampling bias occurs in research, and this can happen even to researchers with years of experience. Therefore, it is important to understand what it is, and how it is introduced into your data, in order to prevent it. In this blog post, we will help you to understand what sampling bias is and how to avoid it in your own customer data.


What is sampling bias?

Sampling bias is a bias that occurs when some members of the intended population have a higher or lower probability of being selected than others. This results in a biased sample of a population in which not all individuals were equally likely to have been selected.


Although a bias in the sample may not always invalidate the data gathered. It does however, limit the extent to which you can generalise the conclusions of your study to the whole population. Since the conclusion derived from the biased sample can only be generalised to populations that share the same attributes with the biased sample.


A common cause of sampling bias lies in the way your data was collected be it surveys or customer feedback. Both of which may favour or disfavour collecting data from certain customers. Researchers typically resort to convenience or judgement-based sampling strategies, in which the samples are selected on the basis of criteria that are somehow connected to the variables of interest.


Sampling Bias in Surveys

A common place area where sampling bias is in surveys. This can be caused by the types of questions asked to customers. Make sure that the questions are relevant and do not lead the respondents into choosing one option over another.


As well as the questions, the design of your survey can also cause sampling bias. Touchpoint Group can help you to create, test and deploy dynamic surveys. After these surveys have been sent and completed, you will then be able to collect all of the feedback and use powerful AI analytics to understand it at scale.

Discover the best questions you should be asking your customers here


How to avoid sampling bias in your customer feedback

To reduce sampling bias in your customer feedback, you may need to take a few steps back and ensure that your survey design process gives equal opportunity for each member of the target audience to be part of your sample group. Get a good grip on what you would like feedback on and ensure the survey distribution method and timing make sense to both you and your customers.

Designing for Success
Not all surveys need to represent an entire company's customer base. Whether it may be to collect feedback from the source to drive NPS and CSAT programs or collect feedback after customer interactions to understand customer experience, it is necessary to understand the real goals and objectives of the survey in order to incorporate it into the design. Touchpoint Group can help you design dynamic surveys to streamline the process of data collection.

Simple Random Sampling
Typically, one of the best methods that you can use to prevent sampling bias is simple random sampling. This is where the selection of the samples is determined entirely by chance. When this method is applied, you increase the likelihood that every customer of your target audience has an equal chance of being selected for your customer feedback surveys. To do a simple random sampling, a computer could be used to randomly pick the participants for the sample from a master list.


Stratified Random Sampling
Stratified random sampling is also known as proportional random sampling or quota random sampling. With this method, you will be able to establish an accurate sample size and examine the population that you identified for your customer feedback surveys.

To do this method, you can begin by dividing a large group into smaller sub-groups known as strata or stratification. The strata are formed based on customers’ shared attributes or characteristics such as income or educational attainment. A sample is then picked randomly from each stratum. As a result, every stratum makes up the same proportion of the sample, preventing any bias in sampling.


Avoid Asking the Wrong Questions
Customer feedback can offer a lot of valuable insights into how to improve your product or service. It is important to note that a dynamic survey should capture the information you need while also not overwhelming your customers. That is why you should know what information you want to collect from the get-go and put your surveys into the right format.

The right answers cannot be obtained if you are asking the wrong questions. Findings in your surveys can easily be compromised by questions that don't adequately cover the scope of the issue that your company is trying to address. An open-ended question, for instance, invites customers to voice their opinions and provide detailed feedback that can help you improve the customer experience. Once you identify your company's key pain points by focusing on two or three issues and gather the appropriate data, you can then guide your data analysis, providing you with valuable information to improve your business performance.

Explore more details on how the quality of data depends on the question you ask here


Sampling bias can easily occur even with experienced professionals. Ensuring that you don’t have biased samples is essential to any customer feedback analysis. With Touchpoint Group, we can help you to send dynamic and unbiased surveys giving you the most accurate customer feedback analysis. Contact our team today to get started. Analyse all your feedback within minutes with our AI Analytics tool saving your team time to get back to what’s important.


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