A small biased piece of information can be enough to change the outcome of an entire study and push decisions worth millions of riyals in the wrong direction. That is the effect of bias: a hidden but powerful error capable of distorting the truth.
Bias is not just a passing slip; it is an invisible deviation that alters facts and distorts the bigger picture, whether in research, decisions, or even the details of our daily lives. The problem is that we often don’t notice it until it has already affected the results and given us misleading impressions. That is why understanding bias and recognizing its types and causes is an essential step for anyone seeking to read data consciously and make more accurate and smarter decisions.
What is bias in surveys?
Bias is any deviation that makes survey results fail to accurately reflect reality. In other words, it is any action or decision that can influence survey results and move them away from the true picture.
Bias can be intentional or unintentional, and it can occur at any stage of the survey, from designing the questions, to the method of distribution, to data analysis. But even the most accurate surveys may still be subject to some unintentional bias, since people’s opinions are naturally subjective and cannot be completely separated from their experiences and attitudes.
Therefore, obtaining accurate and reliable data requires first understanding the potential types of bias and addressing them consciously, so that they do not affect your survey and mislead its results. The goal here is to minimize unintentional bias as much as possible and completely eliminate intentional bias.
How can survey bias affect research and results?
Survey bias can significantly affect the outcomes of a survey by distorting results, leading to:
1. Inaccurate data
When bias infiltrates a survey, the resulting data no longer accurately represents participants’ views. Responses may be insincere or extreme, reducing the credibility of the results.
2. Faulty strategic decisions
Survey bias may push companies or governments to adopt weak strategies or make poor investments. When decisions are built on inaccurate data, efforts may be directed toward ineffective marketing or developing products that do not meet the needs of the broader audience. This ultimately multiplies challenges, weakens expected outcomes, and wastes financial and human resources on the wrong track.
3. Lower return on investment
Survey bias may direct financial resources toward products or marketing campaigns that do not meet actual market needs. As a result, projects fail to achieve expected profits, and ROI decreases directly, since invested funds are not converted into real financial results or tangible profits.
4. Weakened trust and satisfaction
Survey bias may cause stakeholders and investors to feel disappointed with an institution’s performance, leading to reduced market research budgets in the future. Discovering that data is inaccurate or biased can also harm the institution’s credibility and negatively affect trust from customers, employees, and investors.
5. Inconclusive research
Research teams may be forced to repeat surveys to identify the source of the problem, draining time, money, and resources without reaching reliable conclusions.
3 Common Types of Survey Bias
There are three common types of survey bias, each with its own challenges and effects:
1. Sampling Bias: Occurs when the selected sample does not accurately represent the entire target population.
2. Response Bias: Occurs when participants answer questions inaccurately or dishonestly, or under different pressures and assumptions.
3. Interviewer Bias: Occurs when survey creators or interviewers, consciously or unconsciously, attempt to influence the survey process, leading to biased results.
Understanding and addressing these biases is critical to ensure accurate responses from a representative sample.
First, Sampling Bias
Sampling bias (also known as selection bias) occurs when survey participants do not accurately represent the target population. This happens when some groups are overrepresented compared to others, whether intentionally or unintentionally. The result? Incomplete data that does not reflect reality and cannot be generalized to everyone.
4 Common Types of Sampling Bias
1. Exclusion Bias
Also called under-coverage bias, this occurs when certain groups in the target population are deliberately ignored. This produces biased data that does not correctly reflect reality.
Examples of exclusion bias:
If your survey is only in English, people who do not speak English fluently will not be represented.
If the survey is only available online, those without reliable internet access will not be represented.
2. Non-response Bias
Even if you choose the sample correctly, some individuals from the target population may refuse to answer the survey or drop out. This may happen for reasons such as:
They simply dislike filling out surveys.
They drop out due to the length or style of the survey.
They refuse to reveal embarrassing or sensitive information (e.g., a survey about smoking and health).
The survey link does not work properly on their devices.
Their email address is inactive.
They dislike your brand or don’t understand the purpose of the survey.
This means some voices go unheard, leaving the results incomplete. However, it is important to note that in every survey, some people will always fail to respond. The key is to keep this rate as low as possible. If the number of non-respondents exceeds the normal level, your results will be affected by non-response bias.
3. Survivorship Bias
This occurs when researchers focus on individuals or groups that “survived” or “continued”, such as current customers or successful cases, while ignoring those who failed or dropped out. Survivors are often more positive, leading to biased and inaccurate results.
Example of survivorship bias:
A company wants to understand high employee turnover but only surveys current employees, ignoring former employees who could provide real insight into why they left.
If a product’s sales have declined and the survey is sent only to customers who continue to buy it, results will likely be overly positive. Customers who stopped buying, who are most important to understanding the sales drop, are excluded.
The result? Biased and inaccurate findings that fail to reflect reality.
4. Self-selection Bias
Also called volunteer bias, this occurs when only people with strong opinions or intense interest in the topic participate in the survey. Meanwhile, neutral or less interested individuals do not participate. The study results then skew toward those who responded and fail to represent all groups.
Examples of self-selection bias:
In a survey about a sensitive issue such as abortion, people with strong opinions are more likely to participate compared to those with no defined stance.
A teacher wants to measure the usefulness of a training course but makes participation optional. The most motivated and serious students are the ones most likely to respond, so the results reflect only their views and not the entire class.
10 Tips to Avoid Sampling Bias
1. Use an updated random sample: Make sure your participant list includes the correct target groups, and that the sample accurately represents the population. Use free sample size calculators or learn more about representative sampling to ensure accuracy.
2. Define objective inclusion criteria: Set clear rules for determining who participates in the survey. These rules should be precise and objective (not based on personal judgment) to ensure the data collected aligns with research goals.
3. Choose the best method to reach your audience: To avoid survivorship bias, ensure the survey is not limited to only long-term participants.
Example: Instead of sending the survey only to customers who visit a restaurant monthly, send it to everyone who visited in the past six months, including one-time visitors.
4. Encourage participation effectively: Design the survey to be mobile-friendly, use optimized questions, follow up with respondents, and include a clear introduction explaining the survey’s goals.
5. Reduce pre-screening questions and use neutral language: Avoid filtering out participants unnecessarily with questions like “Are you interested in this topic? Yes/No.” Also, use neutral wording so participants can freely express their real opinions.
6. Address language and cultural barriers: Ensure cultural sensitivity and remove language barriers by offering translated materials.
Tip: Explore BSure’s instant translation feature, which supports over ten languages, making it easier to reach multilingual audiences.
7. Pilot test the survey: Run it with a small group first to spot potential bias and adjust the study design for better representativeness.
8. Add options like “I don’t know” or “Not applicable”: This prevents participants from feeling forced to provide inaccurate answers or skip questions.
9. Follow up with non-respondents: Ask them why they didn’t respond and resend the invitation. This helps collect more responses and improves accuracy.
10. Ensure confidentiality: Start the survey with a statement reassuring participants that their answers are anonymous.
Example: “This survey is anonymous, and no one will be able to identify you or your answers.”
In Conclusion
Remember that survey bias directly affects data accuracy and decision success. Understanding its sources and minimizing their impact ensures more reliable data, better decisions, and higher returns, while maintaining the trust of customers and stakeholders.
In short, the goal is not to eliminate bias completely but to control it and improve the quality of results as much as possible. With BSure’s team, you can guarantee better survey results that help improve decision-making and boost business performance.
