Common Sampling Errors In Market Research You Should Avoid

by admin 14 October 2021 3308 Views

The Common Sampling Errors In Market Research You Must Avoid

Statistically, a sample is a subset of a whole population. To conduct research study, it is essential to select a sample group that represents the whole target population. The chosen sample should have the same characteristics as that of the whole target population. When researchers select a sample group that does not represent the target population, it results in sampling error. Selecting the right sample group to yield the most accurate and quality data is not an easy task. So it is important to understand common sampling errors in market research so as to avoid them while conducting surveys.

Why does sampling error occur?

Sampling error occurs because we select and measure characteristics of a sample of the population instead of the whole target population.

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For example, it would be easy to collect the average height of a cricket team by measuring each player’s height but when it is to collect the average height of all the people in the world, it’s impossible and unreasonable to measure everyone’s height to get the accurate result. For that, you have to select a group of people, measure their heights and get an approximate height of the population. Now since you are selecting a group of people i.e sample, your result has some degree of sampling error.

What are the common sampling errors?

Here are the following list of common sampling errors:

Population Specific Error

Population specific error occurs when the researcher fails to know who they have to target for the survey as a sample and doesn’t have a clear picture of the target population. This results in choosing an inappropriate sample for the research study. This error generally happens because of lack of knowledge on selecting a group that would be the most relevant for the research study.

Let’s consider, a company wants to add a new line of product, to capture the attention of the younger generation. But it may be possible that our target population i.e the younger generations may not have the purchasing power. Then, who should be surveyed? The younger generations or the older generations? If the older generation is targeted because of the higher purchasing power it will result in population sampling error.

Selection Error

Selection error occurs when respondents self-select themselves for participating in the survey, meaning that only those participants who are interested take part in the survey.

To efficiently and effectively control the selection error, researchers need to plan a small pre-survey and continuous follow-ups for requesting the target respondents to participate in the survey. CATI Surveys and Face-To-Face surveys are also great ways for maximizing responses.

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Non-Response Error

This error occurs when the respondent doesn’t respond to the survey or they are different from those who respond. Non-response error occurs because the selected respondent was not present at the time of contact or simply refuses to respond. The decrease in sample size and the amount of data collected will result in under or over representation of various demographics.

Sample Frame Error

Sample frame error occurs when researchers select or target the wrong population while determining the sample. This affects the targeted sample as it doesn’t represent the interest of the whole population that the researchers are sampling. Sample frame error generally occurs due to missing some specific demographics or targeting the wrong population segment.

How to reduce these errors?

There are various different measures that can be considered to reduce sampling error, such as:

Increasing the sample size

The size of the sample taken from the targeted population determines the size of the sampling error. The larger the sample size for the survey, the smaller the amount of sampling error as it gets closer to the actual population size and reduces the margin of error.

Improve Sample Design

Sampling error can also be reduced by improving the sample design and dividing the population into groups.

Sample design can be improved by using a type of probability sampling called stratified random sampling. In this, the population is divided into sub-groups having similar composition and these sub-groups are known as ‘strata’. From every group, a sample is selected randomly. Now since all groups are represented in the sample, it reduces the sampling error.

Research Your Target Respondents

While selecting the sample group, study your targeted population so that you can get a clear understanding of who makes up your target population so as to make your survey effective.

A sample management software you must have

Koncept, Teamarcs robust sample management platform is the ideal solution that can provide total control over the project with a powerful workflow that facilitates smooth collaboration among project stakeholders. Keeping the users in mind, Koncept is built to handle with easy to use features where you can customize, create, add, manage and get reports according to the requirements.

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