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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.
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
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. 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. The most common types of sampling errors are selection errors, sample frame errors, population specification errors, and non-response errors. 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. The different types of sampling methods are convenience sampling, cluster sampling, systematic sampling, purposive sampling, and many others. 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. 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. 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.What is the importance of sampling error?
Does sampling error affect validity?
What are the most common types of sampling errors?
What is the difference between sampling error and sampling bias?
What are the different types of sampling methods?
How to avoid sampling errors?
How to calculate sampling error?
What are some examples of non-sampling errors?
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.