In epidemiology and many other areas of study, the three primary forms of bias in research include selection bias, classification (information) bias, and confounding bias. Selection bias results when the sample you are studying is not representative of the target population, so the findings of your study cannot be safely generalized.

Classification or information bias, which is created by systematic errors in the measurement or classification of the exposures or outcomes, occurs when measurements are taken incorrectly or there is a misremembering of a past event.

Confounding bias occurs when there are two variables, and a third variable correlates with both variables, skewing or even reversing the apparent relationship between the two variables.

What is research bias, and what is its importance?

Research bias is a systematic error that causes the results of a study to drift away from the truth at any point in the research process, including study design and publishing.

Unlike random error, which disappears as the sample size continues to increase, bias can remain masked and continue to mislead investors, founders, and product teams.

For decision-makers, biased scientific research may translate into investing in a product that merely looked favorable based on a limited sample that was biased, while omitting a promising innovation because initial data came out skewed.

This is why VCs, startups, and SMEs are finding more and more that they need to audit study design by researching partners like Nexus Expert Research before putting their faith in the numbers.

The three significant kinds of bias in researching

Selection bias: when your sample is not real life

Selection bias occurs when, of the people or data participating in your study, there is a systematic difference from the target population or set of people that you care about.

Common causes are non-random sampling, low responses from essential groups, or loss of some types of people in follow-up.

For example, a startup that surveys a power user sample of the user base only before a major UX redesign will have inflated satisfaction scores and risks failing to find issues for new or casual users; this is one of the easiest examples of research bias.

In the context of clinical and epidemiological studies, selection bias directly affects external validity, and hence, results cannot be generalized to the patients or markets that really matter.

Classification (information) bias: the problem of measurements gone wrong

Classification or information bias occurs when exposures, outcomes, or other key variables are either inaccurately measured or classified.

Typical sources are faulty instruments, non-standard procedures, ineffective interviewing techniques, and rough recall of past events by the subjects.

Recall bias is another classic subtype, where there are more intense memory biases in patients with a disease compared to healthy controls.

Observer bias is another subclass, where the researcher’s expectations impact the way that outcomes are recorded (i.e., rounding measurements, interpreting ambiguous UX behaviors with an interpretive bias in accordance with a possible hypothesis).

Confounding bias: What is the hidden third variable

Confounding bias occurs when an association between a disposition and an exposure to an event is found, where the disposition influences some third factor that is also associated with the outcome but is not part of the causal chain.

Because of this, the observed relationship may be found to be stronger or weaker than, or even opposite to, the actual effect that is causing it.

A basic example: an analysis indicates that teams that utilize a specific tool over analytics raise more funding.

Suppose more experienced founders are both more likely to choose that tool and more likely to raise money. In that case, founder experience becomes a confounder, something that needs to be adjusted for before VCs conclude.

Snapshot table: three core bias types

Bias type	What it means	Simple startup / business example
Selection bias	Study participants are not representative of the target population, leading to distorted estimates of effect.	Only surveying loyal, long‑term customers before a pricing change, then assuming the results reflect the whole market.
Classification bias(information)	Systematic errors in measuring or classifying exposures or outcomes, such as mis‑recorded metrics or faulty recall.	Logging “feature used” whenever a screen loads, even if users never interact with it, which overstates engagement.
Confounding bias	A third variable is related to both exposure and outcome and distorts the apparent association.	Concluding that a new onboarding flow causes higher revenue, when in reality larger enterprise customers were pushed into that flow.

Examples of research bias in the real-world startup and SME research biases

Founders are often familiar with research bias: the feeling one gets when initial product data is fantastic, but adoption is flat when the product is no longer in the early adopter bubble.

This is often learned because of selection bias in early user tests or beta programs (i.e., an over-representation of friendly contacts, technological insiders, or highly motivated volunteers).

Product teams also face information bias when analytics events are improperly implemented or when self-reported survey answers are taken at face value, especially when there are problems with recall or social desirability.

For investors and corporate innovation units, around the financial dimension, these hidden distortions in bias in scientific-style pilot studies can result in the mispricing of risk or missed opportunities.

How to reduce the bias in scientific research

No study is perfect, but there are effective methods, including structured ways of reducing bias in scientific research, to a point where decisions are safer and more transparent.

Relevant design tactics include probability sampling methods, clear inclusion criteria, and proactive efforts to reduce nonresponse or attrition.

To limit bias in information, teams should base standardized measurement procedures, pilot surveys, and interview guides, and use blinding where possible so that observers will not know which group a participant falls in.

To help control for confounding, there are several techniques available to the analyst, including stratifying results, statistical adjustment for known confounders, or even randomization so that possible confounding influences are balanced across a set of groups.

Practical checklist table for decision makers

Question to ask your team	Bias mainly addressed	Why it helps
“Does our sample truly represent the customers, users, or patients we care about, and who is missing?”	Selection bias	Forces the team to identify gaps such as non‑users, churned customers, or hard‑to‑reach segments before generalizing results.
“How exactly was each metric or survey answer recorded, and where could systematic errors creep in?”	Classification bias	Surfaces faulty instruments, inconsistent logging, or leading questions that might systematically distort data.
“Which third variables could explain both the exposure and the outcome, and did we adjust for them?”	Confounding bias	Encourages explicit modeling of alternative explanations before claiming causal impact.

When to become a partner with Nexus Expert Research

When the stakes are high in terms of fundraising rounds, major product pivots, or large-scale clinical/market studies, specialist partners like Nexus Expert Research are capable of designing studies that actively anticipate and minimize the impact of bias in study design.

This includes end-to-end support: sampling plans, instrument design, bias diagnostics, and transparent reporting, which investors and boards can trust.

If you want research results you can trust teams that investors, customers, and regulators can trust.

Nexus Expert Research helps teams not just look past the surface to find out what is truly driving results.