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A sampling error is a type of error that happens when the sample used in particular research is unable to represent the entire population. These types of errors occur quite often, so, researchers have adopted a statistical practice of calculating a margin of error at the time of final results. The margin of error covers the total amount of miscalculation error that indicates the difference between a specific sample and the actual population size.
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There are four types of market research errors that usually occur while sampling:
Population specification error: This is the type of error that happens when researchers aren’t aware of who to survey actually. For instance, an apparel brand is planning to launch a new summer collection for kids. In this case, who should be surveyed? The child or the parents? While the purchasing power lies in the hands of parents, kids can completely influence their decision.
Sample frame error: A sample frame error occurs when a researcher targets the wrong sub-population at the time of selecting a sample. For instance, choosing a sampling frame from a particular telephone directory might have erroneous inclusions as people keep changing their location (majorly cities). Also, when people start un-listing their numbers, that’s when erroneous exclusions mainly occur. In fact, financially strong and wealthy people usually have more than one connection, which leads to numerous inclusions.
Selection error: Selection errors are the types of errors that happen when respondents are given the authority to self-select themselves for participating in the research. In this case, you get responses only from the interested candidates. To efficiently control selection errors, you need to put in extra effort by requesting responses from the entire sample set. With effective pre-survey planning, continuous follow-ups, and clear survey design, you can effortlessly increase the participation rate of the respondents. Also, leveraging CATI surveys and face-to-face interviews is a great way of maximizing responses.
Sampling errors: Sampling errors are a result of disparity that occurs in representing a group of respondents. It usually happens in case of improper sample planning, i.e. when a researcher fails to plan his sample accurately. These types of errors can be eliminated by developing an effective sample design, creating a large sample size that reflects the whole population, or leveraging an online sample for collecting responses.
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Statistical theories are considered to be effective in measuring the probability of sampling errors and most researchers rely on them for controlling the errors in their sample size and population. The size of the sampling error usually relies on the sample size considered by the researcher. While larger sample sizes are known to experience fewer errors, smaller sample sizes may encounter a higher rate of errors. To seamlessly understand as well as evaluate the range of error, researchers use a metric that is popularly known as “margin of error”. Majorly, a confidence level of 95% is desired by every researcher.
The process of identifying sampling errors is easy and so is their reduction. Here’s how you can reduce sampling errors:
By increasing sample size: Using a larger sample size helps to yield more effective and accurate results as the research becomes closer to the true population size.
By creating groups to segment the population: Instead of choosing a random sample, create and test groups based on their size in the population. For instance, if people of a certain demographic constitute 30% of the total population, it’s important to ensure that your research is based on this variable.
By knowing your population well: To reduce sampling errors, it’s essential to understand your population and be aware of its demographic mix. Delve deeper to uncover the demographics that use your product or service and always target the right sample (that actually matters to your business).
As sampling error can be measured, most of the researchers use it for gaining a competitive advantage and estimating the accuracy & effectiveness of their findings in market research.
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