What Is Sampling Bias and How Will Workforce Data Help Me Avoid It?

by Revelio LabsDecember 22, 2022

Sampling bias occurs when the samples of a study are selected incorrectly and do not reflect a true "random" sample. Unlike more blatant forms of bias that can arise during market and workforce research, sampling bias is a hidden threat. It can become a serious issue if ignored, especially as the sampling sizes of your future studies grow. The workforce data tools provided by Revelio Labs can help eliminate sampling bias from your studies. No matter how large your business grows, having the right tools on hand that help you correct any biases that arise from common workforce studies.

Isn’t Bias In Sales Studies Only an Issue for Larger Companies?

While it’s true that bias becomes more complicated to solve as sample sizes increase, bias is a danger to studies (and companies) of any size. Because bias can arise from both the data collection procedure and the design of the study itself, it takes professional tools and experience to perform accurate research.

Here are just a few ways that you can accidentally commit sampling bias:

  • Self-selection bias: people with positive or negative experiences may be more willing to take part in certain studies
  • Nonresponse bias: people may avoid taking part in studies that cover sensitive topics or take too long
  • Undercoverage bias: some members of a population may not be represented in a study due to a lack of technology access
  • Survivorship bias: members of a population who had a positive experience may be more willing to take part in a study

What Types of Problems Can Sampling Bias Cause?

Self-selection and Nonresponse Bias - The Problem With High Emotions

If you have ever stumbled across a product review page where all (or nearly all) of the reviews are either glowing recommendations or nasty negative feedback, then you see the issue with self-selection. If a customer has an extremely positive or negative experience with a product or service, they are more likely to participate in a study that reviews such a service. Nonresponse bias functions a bit differently: if the study itself is poorly designed, people may not take it due to time constraints, poorly-worded questions, or sensitive question topics.

Both of these biases are driven by strong emotions. You can recognize the emotions generated by your study in a number of ways, such as ensuring anonymity for the participants or comparing late responses to early responses during data collection. While voluntary information is valuable, eliminating the bias caused by voluntary responses is key to finding accurate results.

To fix self-selection bias, balance or replace voluntary information with “mandatory” information (i.e., data that is not provided by voluntary surveys or studies, such as government labor statistics and industry classifications). Revelio Labs provides a massive database of published labor records, online profiles, and more to help combat such bias.

Undercoverage or Exclusion Bias - Convenience Doesn’t Mean Complete

Undercoverage or exclusion bias happens when key data is excluded from a study simply because a population was not sufficiently sampled. Exclusion bias is similar, as it fails to take a segment of the population into account due to haste, convenience, or outdated sources.

Avoiding undercoverage bias can be done by conducting research through multiple communication channels. Revelio Labs excels in providing multiple avenues of information so that no demographic or sample size is limited by technology. You’ll never have to worry about information being out-of-date with Revelio Labs, as all workforce data provided by our patented AI-powered software is real-time public information. We even take into account the fact that many work profiles and workforce data are not digital. We add sampling weights to adjust to any underrepresented samples to provide accurate results.

Survivorship Bias - Problems With Failure

Proper workplace research can be difficult for many reasons, though none so much as simply “missing the big picture.” Survivorship bias occurs when researchers concentrate too heavily on entities that “survive” a given set of conditions while ignoring those that do not. In the finance and business world, this often means ignoring companies, products, or people that fail without giving thought to why they failed.

Because correlation may not equal causation, being overly optimistic or pessimistic in estimates can cause problems in current workforce studies. Revelio Labs’ access to global workforce data can help you include the variables that are easy to forget or ignore. Learning from failure can help modern companies grow, especially if the failure “belongs” to someone else. We never place limits on the data we provide our clients. With information from over two million private companies, we can help you identify any gaps that may exist in your business’s game plan.

How Can Revelio Labs and Workforce Data Solve My Sampling Bias?

Overcoming sampling bias can be best accomplished through oversampling. That means relying on the largest sampling size possible. Revelio Labs provides this service at lightning-fast processing speeds. Discover how Revelio Labs can provide your company with real-time data to keep your company maneuverable in today’s modern market. Start your free demo today.

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