10 Types of Research Bias and How to Avoid Them
Rebekah Paul |
 10/03/22 |
8 min read

It’s not possible to eliminate bias in market research, not entirely. Humans being central to the process means that subjectivity is also central to the process. But there are ways to lessen it!


Subjectivity is not necessarily a bad thing. You want people – both researchers and respondents – to express their personality. The researcher can innovate the process and respondents can more fully reveal themselves.

The other side of subjectivity in market research is destructive but also inevitable, up to a certain level.

The goal of any seasoned marketer/researcher is therefore to reduce it to the lowest level possible – the rest is up to the other aspects of the process.

What Is Research Bias?

Research bias is the tendency to skew the market research process towards a particular outcome. It is based on the individuals’ personal experiences and can occur in both the researcher and respondents.

Research bias leads to false conclusions, ultimately affecting the business decisions made by various people across the organization.

Understanding the different types of research bias can help marketers and researchers conduct cleaner processes and avoid the costly decisions.

Additionally, with modern technology, it is possible to minimize the risk of error by reorganizing the market research process. This requires adopting advanced data collection and analysis techniques, refining the role of the researcher, and embracing the personality of the respondent.

So, let us first get a clue of the different possible ways bias could mar your market research process then a glimpse of how modern technology, particularly AI, can help solve these common problems.

1. Design Bias

Design bias occurs when the researcher employs a research strategy based on their own individual preferences when there is a more effective approach. If you recall from our posts about market research, the strategy stage involves assembling the proper tools, techniques, and team.

There are plenty of openings for research bias to creep in at this stage. First, the research question, if influenced by the researcher’s own subjective experience instead of the business needs, will lead the team down the wrong path.

This can be avoided by conducting exploratory research before committing resources to a research problem that’s not properly understood.


Second, the research question may be alright, but the approach is totally wrong. For instance, the researcher may employ the most expedient methods in the place of a more rigorous approach.

Third, the mode of interaction with the respondents may be rooted in bias. Survey questions and interviews may be repetitive – which may be okay sometimes to ask them in different ways to examine responses.

However, if not expertly designed, they may become monotonous, causing habituation bias in the respondents where they get used to answering a certain way

2. Confirmation Bias

Confirmation bias is another researcher bias where research results are accepted just because they affirm the researcher’s subjective beliefs and not necessarily reflective of the situation.

The greatest source of confirmation bias is experience and time investment. The researcher is more likely to accept results that confirm what they already know about the way markets or consumers are than those that contradict their views.

In the modern fast-changing business environment, it’s easy to see why such bias might lead to a colossal failure down the line.

Similarly, when a researcher spends much time and energy on the project, it can be a challenge to objectively interpret the results.

This form of confirmation bias can be avoided by having different people on the team working at different stages: Data collection, analysis, and intelligence application.

Confirmation bias can also be avoided by collecting enough data from a variety of relevant sources.


Of course, this will call for the use of advanced technologies that can gather data easily from multiple locations and have the added advantage of deep analysis of the material.

3. Acquiescence Bias

Sometimes called “yes-saying,” acquiescence bias is a respondent bias. It occurs when the subject feels the need to respond affirmatively even when the proper response would be negative.

It can be rooted in two other types of biases: Sponsor and social desirability biases. Sponsor bias is when the respondent wants to please the researcher because they know the sponsor.

When necessary and possible, the researcher should not reveal the commissioner/sponsor of the study lest the respondents develop acquiescence bias.

Social desirability bias occurs when the respondent provides the information that would make them more acceptable by others.

Example: In a large hall of people, ask this, “By show of hands, let me see how many of us are having affairs.” We guarantee you that results will not be consistent with available data.

But acquiescence bias can get more serious. Respondents may give you positive feedback on your product because they don’t want to disappoint you when they don’t know that the truth would help you make it better for them.

Ensuring privacy of the respondents is a great way to protect your study from acquiescence bias.

4. Selection Bias

Selection bias is also referred to as sampling bias. It occurs when the researcher chooses a sample without regard to its representation of the general population.

No competent researcher will intentionally select a weak sample. However, the criteria they use to identify the subjects and their interpretation of the results may be biased to automatically skew the process towards a particular outcome.

A likely scenario of selection bias is employing one method in a study that requires a multi-modal approach. For instance, administering a survey to the online audience and declaring the results to represent the general audience.

Another form of selection bias is when the relevant demographics are not well represented. For instance, collecting data about social media use by only interviewing millennials.

There are many other such instances. Overall, selection bias can be avoided by developing inclusive criteria as well as proper interpretation of the data keeping in mind its source.

5. Cultural Bias

In the modern globalized economy, cultural bias is one to watch with a keen eye. It is based on the researcher’s assumptions of the respondent in relation to their culture.

It may arise from the researcher judging the respondents from a different cultural background by the standards of their own culture – a concept referred to as ethnocentrism.


Cultural bias may also manifest the recently discussed confirmation bias. In this case, the researcher approaches the respondent with preconceived notions about how they behave and what they prefer.

The remedies used for confirmation bias can work effectively on this form of cultural bias. On the other hand, ethnocentrism can be met with cultural relativism and netnography.

Cultural relativism is the concept promoting tolerance across cultures. The researcher should understand the beliefs and practices of the respondent in the context of the latter’s culture. This makes not just moral but also business sense.

6. Procedural Bias

Procedural bias occurs when the research process is disorderly. In a properly conducted study, the stages in research should follow a predetermined sequence and enough time allocated to them.

Procedural bias may occur if the questions are asked in the wrong order. This may be confusing in some cases causing frustration and mental fatigue.

In other cases, it may manifest in another kind of bias caused by asking leading questions and various forms of acquiescence bias.

The researcher may also cause procedural bias by collecting data on unwilling respondents. They may be unwilling because of personal reasons, being physically forced, etc.

Certainly, the timing and time taken to answer the questions is an important aspect of the procedure. If respondents are caught at the wrong time, they may be unwilling to participate. Similarly, if they aren’t given enough time, they may provide wrong or incomplete information.

There are enough clues in the causes of procedural bias to help find solutions.

7. Analysis Bias

Bias may also occur during data analysis where data samples are prioritized based on the expectations, experiences, and wishes of the analyst.

Some researchers get tripped up by the hypothesis where they grow to expect the results to confirm it. In such a case, the data samples that lead towards the predetermined conclusion will be favored.

The researcher should recognize the hypothesis as merely a starting point for the process. It can be proved or disproved.

There is also a wide open window for cultural bias at the analysis stage. For instance, the analyst’s individual experiences with people from a particular culture may influence how they interpret the data.

Reporting bias may also easily occur under the conditions created by analysis bias. This is where the results are compiled with a tinge of the researcher’s personal beliefs, culture, and other factors.

Analysis bias can be solved through technology. Modern technology can handle a large part of the data analysis stage, leaving humans with very little room for error in interpreting the results.

8. Hot Stuff Bias

Hot topics may generate a lot of interest from researchers, but they may also be poorly researched. This can occur if the researcher is in a hurry to get some results in and publish or share to impress certain people.

A scenario of this is when a subject is trending and studies have already come out in response to the trend. If the researcher finds information that contradicts what’s already out there, they may withhold and only present the positive findings.

Similarly, the researcher may be overly focused on being controversial and hence look for negative information to “debunk” the prevailing notions.

Hot stuff bias can be avoided by following the proper research protocols and allocating the right amount of time to every study. Trends should be analyzed by first collecting enough current and historical data to determine their staying power.


9. Availability Bias

Availability bias refers to the tendency to use information that is most readily available rather than finding that which is most accurate.

It may come up from the foundations of other biases such as confirmation bias, perception bias, and recall bias.

Perception bias occurs when the researcher’s subjective experiences about people and events are carried over into the process. Recall bias is caused by inaccuracy or incompleteness of recalled events and experiences.

Nipping these biases on the bud can prevent their escalating into availability bias.

The use of technology should also be checked as machines may be fed readily available data, do a good job of analyzing it, and it turn out to be wrong as it is not representative.

Before making decisions or even analyzing the data, the stakeholders should consider whether it is sufficient.

10. Confounding Bias

Confounding bias occurs when an association is drawn where none exists. It can also prevent the researcher from seeing a true association masked by the false one.

Research by observation is most prone to confounding bias but it can also occur with other research methods such as surveys.

For instance, a study to find out the attitudes of Gen Z towards a vaccine may find out that the majority of those who have taken it are also active on social media.

The conclusion could be made that social media raises awareness by a certain amount for the vaccine among Gen Z. But the truth may be that most parents of Gen Z are pro-vaccination so they make their children get it.

Confounding bias can be avoided by randomizing the selection of respondents. This minimizes the chance of introducing confounders but it may not be enough.

Stratified sampling, geared towards the best representation of the general population, may be the ultimate solution against confounding bias.

Minimize Research Bias with AI

Human participation is central to the market research process. However, human intervention is also the reason for each of the above-mentioned biases.

The solution for modern researchers is to reduce human intervention using advanced AI technology.

The proliferation of the internet has made available vast amounts of data that can be collected and analyzed objectively without the need for direct interaction between humans.

This alone mitigates much of the respondent bias. With enough data and superior data analysis technology, the researcher bias is also largely taken care of.

Thankfully, most businesses don’t have to develop their research AI in-house. They simply use AI-backed research tools and platforms such as NetBase Quid®.

With NetBase Quid®, you can gather all the data you need from any source on the open web including social media, news media, business websites, review sites, and discussion boards.

Additionally, the platform makes it easy to integrate with other tools such as Rival IQ, your CRM software, and countless others where your data may be lodged. You can even upload legacy data into the platform for a comprehensive analysis.

If you’d like to see how NetBase Quid® can help you minimize research bias through its AI-based tools, reach out for a demo now.


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