Sampling Error In Statistics


Table of Contents

Every research has a target population that is impacted by the results of that research and has the power to provide valuable inputs at the same time. It is out of this target population that a sample of highly representative people are chosen to project their views and opinions.

The accuracy with which the sample gets selected determines the quality of research that gets conducted.

What is a sampling error in statistics?

Sampling Error refers to skewed or distorted research results due to a non- representative sample. Such an error occurs when the sample is not able to project the target population’s ideas , perceptions, choices and preferences. Any judgement or statistical analysis done on the basis of the data provided by such non-representative sample is inconclusive. It is a matter of precaution to eliminate such errors to avoid wastage of time, effort and resources. Determining the number as well as picking out the right individuals is imperative to carry out a thorough research by examining every aspect to the smallest detail and deriving actionable insights.

New call-to-action

Types of sampling errors affecting research


Population selection error: This error occurs when the researcher is unsure about the type of individuals that are to be included as part of the study.

Selection error: Selection error occurs when people volunteer to be a part of the research. Such voluntarism means that only those parties that are willing and interested in responding get included in the sample. Such a selection can hinder important inputs from being included in the research as respondents with a relevant knowledge base capable of presenting the target population’s mindset, may not be interested in taking part.

Sample frame error: Researchers need to be careful about the information from where the eligibility and inclusion of a sample gets decided. The information so used must be up to date and should present precise details about the respondents to certify if they can act as a reliable participant.

Non- response error: Certain respondents may choose to not answer surveys at all. This is mainly due to a lack of incentive to nudge the respondent into answering. This can have adverse effects such as untimely data collection, lack of relevant inputs and poor quality research.

Example of sampling error

A study wishes to examine the online subscriptions that people have in order to identify what prompts them to pay for such subscriptions and how the services offered by such platforms differ from others that they choose not to pay for.

The research includes options such as Amazon, Flipkart, Big basket, Hotstar, Netflix, Paytm mall etc and allows the respondent to mention any other names that have not been provided in the options.

The targeted respondents for such studies are individuals that pay for such subscriptions.

Sampling error can easily occur in such studies when the people that volunteer are majorly the customers of any one or two of the platforms mentioned above. This seriously hinders the quality of research as:

– the end-users are not able to analyse trends about the current subscription platforms that are appealing to customer interest.

– the results will be skewed in favour of one or two platforms that have gained major representation in the self selected sample.

– the users remain in the dark with respect to the target population’s point of views about the platforms that are least subscribed to and the reasons behind the same.

This is a simple example of sample frame error and how it adversely affects research results.

Preventive measures that can help reduce sampling errors


1) Increase your sample size: Apart from the quality of individuals , the quantity of people that are included in the sample is also highly important. Increasing the sample size can help incorporate more ideas and inputs into the study, making it more representative.

2) Use multiple resources: The usage of more than one source of data collection is valuable in verifying results. Secondary resources of data published by other researchers help in getting insights that support accurate data collection.

3) Random sampling: Use random sampling methods to eliminate bias in selecting respondents into the sample population. Random sampling provides an equal opportunity for every individual from the target population to be included into the target population.

4) Segment your sample: Try dividing your sample into small groups based on a basic criteria. This division allows narrow research to take place that studies each segment as a separate and makes note of variations that arise among different groups , resulting in a nuanced research procedure.

Read more