Sampling Error, PDF

What Is a Sampling Error?

Sampling error is a statistical deviation that happens when a researcher fails to select an actual representative sample of a large population of data. This means that the results taken from sampling become invalid due to the error found in the data collection. Market researchers, statistical researchers, and survey researchers who select random samples or who select based on bias, sampling errors will certainly take place because they mostly fail to denote the entire population. 

The common factors affecting sampling errors are the size and design of the sample, sampling fraction, and population variability. Additionally, it is important for all researchers to know the percent errors or how big the sampling errors are during the analysis process because smaller percent errors mean that they are close to the accepted or original value. For instance, a 1% error shows that they got very close to the accepted value. On the contrary, 48% indicates that they were quite a long way off from the real value. 

Types of Sampling and Non-Sampling Errors 

Sampling error does not mean that researchers made mistakes when they select or work with a sample. There are also non-sampling errors that can occur even when they are not sampling as these errors take place when there are issues with the sampling method or the survey design. In this section, we will explain the most common types of sampling and non-sampling errors.

Administrative Error: This type of sampling error is connected to the issues and problems on who the sample is carried out. For example, flaws in the design or the ways of implementing the sample. If the selected sample is an inappropriate representation of the population, it is an administrative sampling error. To avoid or minimize this, be attentive to the design and execution of your sample. Random Sampling Error: This sampling error is due to chance and is impossible to be avoided. It can be reduced, but it can not be totally removed when researchers increase the sample size. Selection Error: This non-sampling error happens when respondents are the ones who select their participation in the research study which means those who are interested to join will respond. It is a non-sampling error that can be managed by improving the methods of data collection. Sample Frame Error: When a researcher selects a sample from incorrect population data, a sample frame error may occur. This error also happens when a tester accidentally includes respondents from outside the target population. Population Specification Error: Researchers encounter this non-sampling error when they did not completely understand who they need to survey. For example, they are making surveys on the consumption of breakfast cereals in families. They consider the shopper in the family who makes the purchase decision, still, the children influence the choice of cereal. This error can be prevented when researchers understand their research question thoroughly before they start preparing a questionnaire form or selecting respondents for the survey research. Non-Response Error: It is a type of non-sampling error that happens when survey respondents are different from those who don’t respond. It may appear because either the prospective survey respondent was not contacted or refused to respond. Researchers must check the extent of the non-response error by conducting follow-up surveys using alternative methods.

Ways to Avoid or Reduce Sampling Errors

It’s hardly possible to quantify the sampling error degree in a study since it is very challenging to collect the relevant data from the whole population you are studying. There are simple ways to correct and reduce sampling errors to reduce the amount of uncertainty in research studies. 

1. Identify and Understand Your Population

It is fundamental that you thoroughly identify and understand your target population and its demographic aspects before you select a sample. Research and analyze your target population effectively so that you can clearly outline your target population and target the subpopulation appropriately.

2. Increase Sample Size

Increase the sample size so that your study gets closer to the actual population size. This makes the sample more representative of your target population and lowers the margin of error. 

3. Improve Sample Design

Use stratified sampling, a type of probability sampling, to divide a larger population into homogenous sub-groups so that you can improve sample design. This method helps you to make sure that the sample group has a similar composition to that of the target population, being more representative of it.

How to Calculate Sampling Error

Testers manage some level of inaccuracy in their discoveries frequently because it is often hard for a larger population to participate in a study. It is essential to calculate sampling error so that researchers are able to estimate the amount of uncertainty in sample size and determine whether the sample size is unsatisfactory or unrepresentative of the larger population. When researchers calculate statistical errors, they may use the sampling error formula: Z x (σ /√n). This is where Z is the Z score value based on the confidence interval, σ is the population standard deviation, and n is the size of the sample. Follow this step-by-step guide to calculate sampling error.

Step 1: Search for the Sample Size and the Standard Deviation

Review your case study to find the sample size. For example, if your sample included 80 people, use 80 in the sampling error formula. Researchers use standard deviation to express the population’s variability as it indicates the average difference between all the actual scores of the subjects from the mean or average of the overall scores. Calculate the standard deviation by using the parameters of your population and the average value of your data. If you are unfamiliar with the standard deviation, search the formula for sample standard deviation.

Step 2: Measure Your Confidence Level

Utilize the confidence interval for your sample size as it specifies the range of your sample value that more likely contains the accurate population mean. Multiply half of the confidence interval by the square root of the sample size. Divide this by the sample standard deviation to measure your confidence level. Then, use a confidence level table to figure out your equivalent score.

Step 3: Calculate the Square Root of the Sample Size and Divide It by the Standard Deviation

If the square root of your sample size is a whole number, your sample size may be a perfect square. For example, 25 is a perfect square because it has a square root of 5, while 12 is not a perfect square because it has a square root of 3.46. After that, divide your population’s standard deviation by the square root of the sample size. Use a sample size calculator and record the result.

Step 4: Multiply the Result Based on the Confidence Level

Lastly, multiply the result by the confidence level score. Use a calculator to work with your complex decimals. The final product serves as the sampling error for your research study.

FAQs

What is the importance of sampling error?

Sampling error is important because it describes research study results, how much they differ, and the statistical confidence level that can be established in them.

Does sampling error affect validity?

Sampling error does not affect validity. It only measures reliability since researchers and data analysts can’t estimate how representative the panel is of the larger population.

What are the most common types of sampling errors?

The most common types of sampling errors are selection errors, sample frame errors, population specification errors, and non-response errors.

What is the difference between sampling error and sampling bias?

Sampling error is a particular example of wrong sampling as the estimate does not represent the population. On the other hand, sampling bias is a constant error that affects several samples.

What are the different types of sampling methods?

The different types of sampling methods are convenience sampling, cluster sampling, systematic sampling, purposive sampling, and many others.

How to avoid sampling errors?

To avoid sampling errors in your case study, increase the population size and make sure that the majority of the selected respondents fully represent the rest of the population. It is essential that you carefully sample and search for the proper candidates for your survey research. 

How to calculate sampling error?

When you calculate sampling error, make sure to record the sample size, search the standard deviation of the population, and measure your confidence level. Afterward, calculate the square root of the sample size. After that, divide the standard deviation value by the square root value. Lastly, multiply the resulting number by the confidence level score. 

What are some examples of non-sampling errors?

Some examples of non-sampling errors are biased survey questions, biased processing/decision-making, data entry errors, false information provided by respondents, inappropriate data analysis conclusions, and non-responses.

To generate accurate and reliable results for your business market research and survey research, it is integral that you fully understand how sampling errors and the common types of non-sampling errors work. Despite the complexity of working with samples and avoiding statistical errors, there are effective solutions that you can use to avoid confusion in your business. Also, being well-informed about sampling errors can guide you to prevent misjudging your market and creating costly mistakes. To minimize and control a sampling error, determine your population, increase the sample size, perform an external record check to confirm the consistency of recorded data, remove bias, and design samples into groups. Access, download, and use PDF templates from Sample.net for controlling and managing sampling errors in your market and survey research work such as survey reports, research survey templates, student survey reports, market research reports, and survey analysis reports.