Types of Sampling Errors And How You Can Avoid Them

Sampling errors can be caused by a range of different causes. By understanding what causes sampling error, we can take measures to minimize it. 

The following is a list of the most common types of sampling errors. 

 1. Sampling frame error

This occurs when the sample is selected from the wrong population data or if the sample doesn’t accurately represent the target population. This error generally includes targeting the wrong population segments or completely missing out on certain demographics within the correct segments. 

Mitigate this type of sampling error by: 

• Evaluating the list or source from which you draw the sample to ensure it accurately represents the population you are interested in studying. 
• Expanding the sampling frame of your research to include any segments that may have otherwise been overlooked. 

 2. Selection error

This error occurs when you systematically exclude a sample or participants who opt to participate in the study, and therefore, only those interested participate in the survey. This can lead to gathering biased opinions, resulting in distorted outcomes. 

If researchers overlook respondents who didn’t initially respond, the outcome will not reflect the target market. If, instead, the researcher decides to follow up with the respondents who didn’t initially participate in the survey, the outcome is very likely to change.   

Avoid selection error from harming your research findings by: 

• Utilizing random sampling techniques to ensure every individual of the intended population has an equal chance of participating in the study. 
• Minimize self-selection as a way to gather respondents. 
• Encourage diversified participation by offering incentives or ensuring anonymity. 

3. Population specification error

This is a type of sample design issue that is caused when a researcher fails to outline who they want to survey clearly and therefore, does not have a clear idea of their target population. When you don’t have a clearly defined target population, you may select inappropriate elements to be a part of your sample group. 

Mitigate this type of sampling error by: 

• Defining the population of interest with specific criteria based on the research objective will ensure clarity and help you understand whether the sample is relevant to the research purpose. 
• Using random or stratified sampling methods to ensure a representative sample. 

4. Non-response error

Non-response errors occur from the failure to obtain responses from all units in the selected sample group. The decrease in the sample size and amount of information collected will result in a larger standard error. Additionally, a bias is introduced at the risk of non-respondents differing from the respondents within the selected sample. 

 Address non-response errors by:

• Using follow-up surveys to encourage participation and improve survey response rates. 
• Monitor survey progress in real-time to identify non-response patterns and adjust sampling strategies accordingly. 

5. Coverage errors

This type of sampling error arises when your sampling frame does not include all the relevant members of the population. It can occur due to outdated sources of samples or inadequate coverage of certain segments. 

Mitigate coverage errors by: 

• Regularly updating the sample frames so they reflect the population and maintain relevancy. 
• Leverage multiple sources to create the sampling frame.