Research Writing

How to reduce sampling error/common sampling errors

Today we will learn what a sampling error is and how it can affect our investigation. In general, these are problems related to the representativeness of the sample. This is the case when the population is poorly focused or the sample size is too small, or when the response rate to survey questions is not high enough, etc. How to reduce sampling error?

Recall that sampling is the process of extracting information from a fraction of a large group or population to draw conclusions about the population as a whole.

Therefore, its purpose is to provide a sample that represents the population and reproduces as faithfully as possible the main characteristics of the population under study, hence the importance of not falling into a sampling error.

What is a sampling error?

A sampling error occurs when the sample used in the study is not representative of the entire population. Sampling errors often occur and therefore researchers always calculate a margin of error during the final results as statistical practice.

The margin of error is the amount of error allowed for a calculation error to represent the difference between the sample and the actual population. How to reduce sampling error?

What are the most common sampling errors?

Now that you know what a sampling error is, let’s find out what the four main sampling errors are in market research:

Population specification error: A population specification error occurs when researchers do not know exactly who to survey.

For example, imagine a research study on children’s clothing. Who is the right person to survey? It can be both parents, just the mother or the child. Parents make purchasing decisions, but children can influence their choice.

Sample frame error: Sampling frame errors arise when researchers wrongly target the subpopulation when selecting the sample.

For example, choosing a sample frame from the phone book can be a mistake because people change cities. Erroneous exclusions occur when people prefer to remove their numbers from the list. There are households that can have more than one phone line, which leads to multiple inclusions.

Selection error: A selection error occurs when respondents self-select to participate in the study. Only those interested respond.  How to reduce sampling error?

Selection errors can be controlled by going to the extra step to request responses from the entire sample. Pre-survey planning, follow-ups, and a clean and orderly survey design will increase your survey participation rate. Also, try methods like CATI and face-to-face interviews to maximize responses.

Sampling errors Sampling errors occur due to a disparity in the representativeness of the respondents. It occurs mainly when the researcher does not plan his sample carefully.

These sampling errors can be controlled for and eliminated by creating a careful sample design, having a sample large enough to reflect the entire population, or by using an online sample to collect survey responses.

How to control a sampling error?

Statistical theories help researchers measure the probability of sampling errors in sample size and population.

The size of the population sample primarily determines the size of the sampling error. Larger sample sizes tend to have a lower error rate.

Researchers use metrics to understand and evaluate the margin of error. Typically, a 95% confidence level is considered to be the desired confidence level.

What are the steps to reduce a sampling error?

Sampling errors are easy to identify. Here are some simple steps to reduce the sampling error:

Increase the sample size : A larger sample size results in a more accurate result because the study is closer to the actual size of the population.

Divide the population into groups: Test groups according to their size in the population, rather than using a random sample. For example, if people in a certain demographic make up 20% of the population, make sure your study takes this variable into account.

Know your population: Study your population and understand their demographic mix. Find out which demographics are using your product and service and make sure you only target the sample that matters.

Sampling error is measurable, and researchers can use it to their advantage to estimate the accuracy of their findings and estimate the variance.

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