Contents01 The buzz about survey bias—and why it’s got to go 02 tactics for designing a survey without bias03 The future of limiting survey bias
Head to your nearest Little League diamond and ask the parents watching the game, “Who’s the best player out there?”
Odds are, at least a few will say something like, “Number 9. But I’m biased; she’s my kid.” These proud parents display a powerful universal truth. Everyone has experiences, personal connections, and preferences that sway them toward some ideas and away from others. These are their biases.
Here’s the challenge: Bias exists in all forms of research, and it can show up at any stage of the research process. Left unchecked, bias leaves survey results invalid and unreliable.
James Crease, Typeform’s Director of Research, and David Dalati, Senior Product Researcher at Airalo, are seasoned survey specialists. We’ll dig into their best practices to defeat bias and collect meaningful data that rings true.
The buzz about survey bias—and why it’s got to go
Brands that take the time to survey their audience want to know the truth about their customers’ satisfaction and pain. But a biased research design gets in the way of that goal.
Bias happens when a survey influences respondents to choose one answer over another. It guides people to answer differently from how they truly feel or behave. This means the data may not be objective or accurate, so the survey results are ultimately flawed. When bias is present in a question,
For instance, some topics—such as voting or healthy eating—are subject to social desirability bias. Most people feel that they should exercise their right to vote or get their daily serving of greens, so they often answer based on what they feel is more socially acceptable than their actual behavior.
Survey designers and creators can fuel this bias in the question by signaling that a certain response is the right one. Or they can craft questions to make people feel that every answer is valid. These efforts produce more accurate, less biased results.
Bias places a misleading filter over reality. Well-designed research leads to informed decisions, but biased research can send a company far off-course.
If a brand team conducts research to inform their business strategy, a biased data set could signal that the market is heading in a different direction than it actually is.
When product teams survey customers to understand the user experience, biased questions lead them to bet it all on the wrong features.
Marketers often use surveys to create industry reports. If results are heavily biased in favor of their brand, audiences may sense that the data is skewed and altogether false. Their company’s credibility will dip as a result.
In each of these instances, bias leads to disastrous results for the business. That’s why teams that conduct survey research need to learn to recognize (and eliminate) bias in its many forms.
Marketers might want to ask, “How much do you love using our awesome product?” But that question is a recipe for biased responses. It places major pressure on the audience to answer positively.
“Do you like Marvel movies more than DC?” might be a fine conversation topic, but as part of a survey, it’s a leading question. The phrasing assumes that the audience likes Marvel films to begin with, and it pushes them in a particular direction.
Want better survey results that lead to solid decisions? Learn to limit bias in research design.
4 tactics for designing a survey without bias
Teams should do everything they can to build surveys without bias. Well-designed research uncovers respondents’ honest feelings and experiences instead of researchers’ preferred or assumed outcomes.
Treat these four tactics like checkpoints to check for bias at every turn during the research design process.
1. Plan your hypothesis without a bias
Remember the scientific method from grade school? Get out those goggles and lab coats—it’s useful for survey research, too.
The first step is asking a question to guide the purpose of the research. James describes this earliest stage of research like this: “Take a step back and be really clear on, What is the decision that is going to be made as a result of doing the research? What information do we need to be able to make that choice?” The answers to those questions reveal the ‘why’ behind the research.
Now, it’s time to form a hypothesis—a statement about what the team believes the research will reveal.
The research hypothesis serves as a starting point for the survey—not a benchmark researchers are working toward. If marketers conduct a survey in hopes of confirming a hypothesis instead of testing whether or not it holds up, that hunch will skew the data.
Confirmation bias happens when researchers design surveys in a way that supports their hypothesis. David says that rationale might sound like this: “I have this specific assumption that I want to validate with research, and I'm continuing on with it. I'm going to find the data that I think proves my assumption.” Avoid this attitude and, instead, focus on seeking truth—whether it matches your hypothesis or not.
A great way to form a strong hypothesis before conducting survey research is to start with qualitative research like customer interviews. Conversations with real people can reveal how the audience thinks and debunk internal assumptions that could bias the hypothesis or research design.
2. Write great questions for your typeform
Every question is a chance for bias to creep in. Researchers need to be vigilant about asking the right questions in the right way. These do’s and don’ts offer a framework for avoiding question bias at each step.
❌ Don’t ask leading questions
The way you ask a question makes a huge difference. Phrasing can influence respondents and bend their answers away from the truth.
Questions like “How much do you like X product?” or “How positively do you feel about Y feature?” can make people feel pressured to answer positively instead of giving their honest opinion. Instead, use neutral, balanced language. “What is your experience with this product?” or “Rate our product on a scale from 1 (I hate it) to 5 (I love it)” gives respondents the chance to consider their honest answer.
Try asking someone who has no stake in the survey (and wouldn’t care about the results) to review the questions for bias. They might spot something that the survey makers would struggle to see for themselves.
Keep questions balanced by offering equal positive and negative responses for Likert scale questions. If a question gives more “satisfied” options than “dissatisfied,” answers will likely lean positive—a balanced scale lets people weigh their options evenly.
✅ Do stay mindful of question order
Anyone who’s shared the good news before the bad news to put their parent or partner in a better mood knows that order matters. However, an unbiased survey should avoid this behavior.
For instance, a survey might ask respondents to rate their satisfaction with a number of popular product features, which could incline them positively. If a more general product satisfaction question follows, they might be swayed toward a better review, even if they’ve had a neutral or negative experience.
For this reason, order survey questions from most general to most specific. That way, more nuanced topics can’t bias someone’s overarching opinion.
❌ Don’t close off a question that might have more answers
Multiple-choice questions should capture all possible responses. While the first several choices may cover a majority of cases, they don’t always capture every respondent’s experience.
Offer an “other” response or an open-text option that allows people to answer in the way that best fits them. Without this additional option, they may simply pick another answer that they deem “best,” which affects your data’s accuracy.
✅ Do keep the survey focused
No one wins with an endless survey. Once the audience gets tired or bored, they often start reading too quickly or just picking the first response—and that’s a recipe for unhelpful survey results.
Remember to start with the purpose of the research—this will help you keep the survey as focused as possible. Limit the number of questions to only what’s necessary.
Ali Grimaldi and Tzeying Cheng, the UX experts behind Bellini Slushie recommend staying under 10 questions total—and asking no more than two open-ended questions. (Looking for a resource for unbiased questions? Their question bank is a great place to start.)
James knows firsthand how easy it can be to add a question here or there when building a survey. His advice? “Start somewhere else, and write out your entire questionnaire script and flow—finalize every word and every detail before you start building it into Typeform.”
The more targeted the survey, the more likely it is that respondents will be able (and willing) to answer well till the last question.
3. Analyze the data correctly
Once responses are in, it’s time to put on that analyst hat and dig into the results—and data analysis plays an important role in limiting bias.
Start by cleaning the data and looking for outliers. Ideally, every survey would be the right length to keep people engaged, and respondents would give careful thought and attention to every answer. But in the real world, such a data set is rare. Researchers need to identify and account for responses that could skew the data.
James recommends looking out for these red flags in the data set:
🚩 Does anyone offer clearly contradicting responses that are incompatible with one another?
🚩 Does anyone answer only on the far end of the scale or click the first answer every time? (James calls these “flatliners.”)
🚩 Has anyone completed a form so quickly that it’s next to impossible that they read every question and answered it thoughtfully?
Any of these patterns can indicate that someone’s responses may not reflect what they think.
Once a research team identifies such outliers in the data set, James says, “Then you can make some choices. Do we carry on including those? Or should we delete them from the analysis?” Weigh the impact of these responses on the data set. Removing or keeping them can carry its own form of bias—seek out the approach that'll impact results the least.
4. Find your bias, then test again without it
Sometimes, a survey still returns a skewed data set, despite everyone’s best unbiasing efforts. If thorough analysis reveals a flaw in the research design, you may need to make changes to the survey and run it again.
David says that a study with biased, inaccurate data is essentially useless.
Researchers can try to salvage the study by analyzing what went wrong, he notes. “But it can also be tricky to do that because the entire design from the very beginning has faulty research questions,” David explains. “If all the questions were geared in a specific direction, then it's already been biased, and it needs to be conducted again.”
Revisit the survey design, and identify where the bias occurred. Was it how the questions were phrased? Question order? Answer options? Figure out where bias crept in, and then resolve these issues by asking questions in a more neutral way, limiting order bias, and randomizing the order of answer options.
With a more conscious design, remixing research can produce better results that drive stronger decisions.
The future of limiting survey bias
AI is poised to play an ever-bigger role in marketers’ toolkits, and survey bias is one area where it can help. Typeform is rolling out new AI-powered features that’ll empower marketers to build better forms. Stuck on wording for a survey question? Use AI to optimize question text with the click of a button for better, less biased surveys.
After the surveying is done, AI tools like Typeform’s Holler step in to make reviewing the data a breeze. Whatever questions marketers have about the insights and whatever action items survey responses surface, Holler’s got the answers for faster analysis.
Bias poses a major research challenge with sky-high stakes. But marketers don’t have to do it alone. Typeform is here to offer survey support, AI tools, and research resources to make every form the best—and least biased—it can be. Get started for free today.