What are sampling and non sampling errors and how can a researcher control them?

How to Reduce the Sampling Error for Accurate Survey Campaigns

The sampling error is one of the most common manifestations of the difficulty in performing market research. Even the most effective survey studies wind up falling prey to some kind of error.

Usually, it occurs as the sampling error this is because it is impossible to study every member making up your target market or other target population, regardless of the means you use.

Since this error is prevalent, it is a common statistical practice to measure it before doing a survey analysis of your final results.

This article explains the sampling error, how it differs from the margin of error and other errors, along with how to reduce it for your market research survey and broader campaign.

Defining the Sampling Error

A sampling error is one of the two major errors that market researchers contend with; the other is known as a non-sampling error. While the non-sampling error includes a range of errors brought forth from human mistakes, such as incorrect data entries and questionnaire setup, the sampling error represents a specific phenomenon.

The sampling error refers to the error that emerges when the sample used in your study is not representative of its entire population.

This error arises due to sampling (examining a sample of your target population in your study), rather than studying all the members of its population. Since performing the latter is not practical or possible, the sampling error is a common occurrence in market research.

The sampling error is one of the biggest contributors to the difference between an estimate and the actual value of the population parameter. For the most accurate results, researchers ought to minimize this error.

It should not be confused with the margin of error, a key metric for understanding your sampling error and taking action on it.

How the Sampling Error Differs from the Margin of Error

The sampling error and margin of error may be used interchangeably, but they differ on a few major accounts.

Firstly, the sampling error is a presumed and statistically-backed error omnipresent in market research techniques. For example, in market research, the target population of a study is usually a businesss target market. Since surveys (or other market research means) speak only to a sample of a population, the survey results wont be in perfect alignment with every member present in that target market.

Thus, unlike the margin of error, which gives a precise measurement, the sampling error is a metric used in theory, but not as a specific calculation.

As such, the sampling error is unknown. On the contrary, the margin of error can be determined, when you use random survey sampling methods.

Thus, when the sample is random, you can calculate the margin of error, which is the maximum likely size of the sampling error. Specifically, the margin of error denotes how close a market researcher can expect survey results to be in relation to the actual population value.

Also called the confidence interval, the margin of error represents the difference between the sample size and the exact population. Expressed as a percentage, it exists as an either negative or positive margin, meaning a certain percent lower or higher than the results.\

For example, if the margin of error is 5% at a 95% confidence level, researchers can expect the results to be within 5 percentage points of the true population value, 95 times if the survey was conducted 100 times.

The Two Most Common Sampling Errors in Market Research

The sampling error manifests in two common ways. In all of these examples, the error is exhibited as an incorrect representation of a target population. For market researchers, this often concerns the target market, i.e., the population of your customer base most likely to purchase from you.

1. Over-calculation: Occurs when the sampling error has a margin of error that goes above the percentage in the results. For example, a + 4% error shows that there is a 4% higher percentage between the sample and the actual population.
2. Under-calculation: Occurs when the sampling error exhibits a margin of error that falls below the results of the actual population. For example, a 4% error shows that there is a 4% lower percentage amount between the sample and true population.

While these two common errors occur as sampling eros, there are four kinds of market research eros that occur within sampling. The sampling error itself is the most prevalent kind.

How to Reduce the Sampling Error for Accurate Results

Market researchers can reduce the sampling error sure to rear its head in various survey studies. There are a number of best practices that can lessen this unwanted, though commonplace aspect of market research.

Here are several methods to stave off the sampling error.

1. Increase the sample size. Doing so will yield a more accurate result, since the study would be closer to the true population size.
   1. When increasing your sample size, make sure to keep the quotas for each demographic selection and screening question to be commensurate.
2. Split the population into smaller groups. Use groups proportional to their existence in your overall target market. For example, if 40% of your target market consists of a certain demographic, ensure that you use 40% of this demographic in your survey study.
   1. This can be achieved via market segmentation, which helps you understand the segments making up your overall target market.
      
      
3. Use random sampling. This does not mean your sampling pool will be haphazard.
   1. Instead, it requires using a more diverse, yet precise approach to gaining respondents for your survey.
   2. For example, you can draw a random sampling of respondents, but still control who can take part in the survey, based on their demographics and psychographic information, along with screening questions that they need to respond to in a certain way to enter the survey.
4. Keep tabs on your target market. To survive in business and perform accurate market research, you ought to have a deep understanding of your target market.
   1. You can accomplish this via continuous studies and segmentations.
   2. Running survey campaigns can help you understand your customers and general target population in totality.

Circumventing the Sampling Error

While the sampling error is omnipresent and it is impossible to fully eliminate, you can take precautions and actions that minimize this issue. But first, you must assure that your survey campaign is on the right track, as there are several other errors and biases that surveys are vulnerable to.

To assure that you are setting your survey campaign up for success, you must accompany your campaign with the proper online survey tool. Such a tool can make or break your market research campaign.

This is true on several accounts: the online survey platform serves as the complete home of a survey, as it serves its foundation, forms the requirements of the respondents, creates the option of incentivizing respondents, deploys the survey and works out its results.

Additionally, the survey platform you choose helps reduce the sampling error by way of some of the aforementioned best practices. For example, this platform should allow you to split your target market into groups, use random sampling and facilitate the other two best practices.

Therefore, you should invest in a strong online survey platform, as the platform itself curbs the sampling error your market research campaign is sure to incur.