What is a sampling error?
A sampling error occurs when the sample used in the study is not representative of the whole population. Sampling errors often occur, and thus, researchers always calculate a margin of error during final results as a statistical practice. The margin of error is the amount of error allowed for a miscalculation to represent the difference between the sample and the actual population.
What are the most common sampling errors in market research?
Here are the top four market research errors while sampling:
- Population specification error: A population specification error occurs when researchers don’t know precisely who to survey. For example, imagine a research study about kid’s apparel. Who is the right person to survey? It can be both parents, only the mother, or the child. The parents make purchase decisions, but the kids may influence their choice.
- Sample frame error: Sampling frame errors arise when researchers target the sub-population wrongly while selecting the sample. For example, picking a sampling frame from the telephone white pages book may have erroneous inclusions because people shift their cities. Erroneous exclusions occur when people prefer to un-list their numbers. Wealthy households may have more than one connection, thus leading to multiple inclusions.
- Selection error: A selection error occurs when respondents self-select themselves to participate in the study. Only the interested ones respond. You can control selection errors by going the extra step to request responses from the entire sample. Pre-survey planning, follow-ups, and a neat and clean survey design will boost respondents’ participation rate. Also, try methods like CATI surveys and in-person interviews to maximize responses.
- Sampling errors: Sampling errors occur due to a disparity in the representativeness of the respondents. It majorly happens when the researcher does not plan his sample carefully. These sampling errors can be controlled and eliminated by creating a careful sample design, having a large enough sample to reflect the entire population, or using an online sample or survey audiences to collect responses.
Controlling your sampling error
Statistical theories help researchers measure the probability of sampling errors in sample size and population. The size of the sample considered from the population primarily determines the size of the sampling error. Larger sample sizes tend to encounter a lower rate of errors. Researchers use a metric known as the margin of error to understand and evaluate the margin of error. Usually, a confidence level of 95% is considered to be the desired confidence level.
Pro Tip: If you need help calculating your own margin of error, you can use our Margin of Error Calculator.
What are the steps to reduce sampling errors?
Sampling errors are easy to identify. Here are a few simple steps to reduce sampling error:
- Increase sample size: A larger sample size results in a more accurate result because the study gets closer to the actual population size.
- Divide the population into groups: Test groups according to their size in the population instead of a random sample. For example, if people of a specific demographic make up 20% of the population, make sure that your study is made up of this variable to reduce sampling bias.
- Know your population: Study your population and understand its demographic mix. Know what demographics use your product and service and ensure you only target the sample that matters.
We have also created a tool to help you determine your sample easily: Sample Size Calculator.
A sampling error is measurable, and researchers can use it to their advantage to estimate the accuracy of their findings and estimate variance.