Key takeaways
- Untested assumptions fill gaps in data and experience, and they often harden into strategy without anyone recognizing them as beliefs.
- Their cost shows up as misdirected investment, slow learning, failed launches and repeated debates that evidence could have settled.
- Assumptions about why people behave as they do are especially risky because they are rarely checked against observation.
- An assumptions register, ranked by risk and tested through hypotheses and experiments, turns hidden risk into managed learning.
Where untested assumptions come from
No organization has complete information. Data is fragmented, research is limited and time is short, so people fill gaps with beliefs. Many of these beliefs come from experience and are broadly correct. Others are inherited from earlier conditions, borrowed from other industries, or simply plausible stories that no one has had reason to question. Over time they become part of how the organization talks about its customers, patients, employees and markets, and they stop being recognized as assumptions at all.
Untested assumptions are especially common in areas that involve human behavior. Leaders assume customers leave because of price, that clinicians resist a tool because they dislike technology, that employees disengage because of pay, or that a sales team needs more leads. Each assumption suggests a particular solution, and each solution may consume significant budget and effort. When the assumption is wrong, the solution addresses the wrong problem, and the real cause continues undisturbed.
Why assumptions persist
Several forces keep untested assumptions in place. Confirmation bias leads people to notice evidence that supports what they already believe and to discount evidence that does not. Seniority can make certain beliefs difficult to challenge, particularly when they belong to the people who built the business. Success also protects assumptions: if results are acceptable, there seems little reason to examine the beliefs behind them, even if results could be much better.
Organizational structure plays a role too. When data lives in disconnected systems, no single team sees the whole journey, so each fills its blind spots with assumptions about what happens elsewhere. Marketing assumes something about sales, sales about service, and service about product. Daniel Kahneman's observation that people construct coherent stories from whatever information is available applies to organizations as much as individuals. A confident narrative can feel like knowledge even when much of it rests on guesswork.
- Confirmation bias filters out disconfirming evidence.
- Hierarchy makes some beliefs costly to question.
- Acceptable results discourage scrutiny of underlying beliefs.
- Data silos force each team to guess about the others.
- Coherent stories feel like evidence even when they are not.
What untested assumptions cost
The cost of untested assumptions is rarely visible as a single line item, which is part of why it is underestimated. It appears as investment in solutions that do not move the outcome, such as a pricing change that fails to reduce churn because churn was driven by onboarding friction. It appears as slow learning, when teams cannot tell why an initiative worked or failed because the theory behind it was never stated. It appears as recurring debates in leadership meetings that evidence could have settled long ago.
There are also opportunity costs. While resources go toward the assumed problem, the real drivers of performance go unaddressed, and competitors who understand them better gain ground. In health and social programs, the cost can be measured in people who did not get the help they needed. Inside organizations, wrong assumptions about employee behavior can produce policies that damage engagement and trust. None of these costs require bad intentions. They follow naturally from treating beliefs as facts.
How to surface hidden assumptions
The simplest way to surface assumptions is to ask, for any plan or decision, what would have to be true for it to succeed. Writing those conditions down often reveals beliefs that no one has tested. Another approach is to map the journey behind an outcome and mark each point where the organization lacks direct evidence about what people do or why. Whatever fills those gaps in current thinking is an assumption, and it should be labeled as such.
Qualitative research is a powerful tool for this work. Interviews, observation and usability testing frequently contradict internal beliefs, and the contradictions are valuable precisely because they are surprising. Pre-mortems, where a team imagines a future failure and explains its causes, can also surface hidden assumptions. The goal is an assumptions register: a written list of key beliefs, each rated by how much the plan depends on it and how much evidence currently supports it.
- Ask what must be true for the plan to work.
- Mark evidence gaps along the customer or employee journey.
- Run interviews and observation to challenge internal beliefs.
- Hold a pre-mortem to imagine how the plan could fail.
- Rank assumptions by dependence and current evidence.
From assumptions to hypotheses and tests
Once assumptions are visible, the riskiest ones should be converted into testable hypotheses. An assumption such as customers leave because of price becomes a prediction: customers who leave will cite price more often than other reasons, and a price change will reduce departures among a defined segment. That prediction can be checked with existing data, a survey, interviews or an experiment. Some assumptions will be confirmed, which provides confidence. Others will be refuted, which redirects investment toward what actually matters.
Testing should be proportionate. High-stakes assumptions behind major investments justify careful experiments or pilots. Lower-stakes assumptions may only need a quick data check or a handful of interviews. The discipline matters more than any single method: before committing significant resources, identify which beliefs the plan depends on, and test the ones that are both important and uncertain. Over time, this becomes a habit that improves the quality of every decision.
How Datalidy addresses untested assumptions
Surfacing untested assumptions is an explicit step in Datalidy's Behavioral Signals Design methodology. After mapping the journey and the data around an outcome, the firm identifies where assumptions are quietly filling gaps, often at the boundaries between systems or departments, and rewrites them as testable hypotheses paired with predictive KPIs. In the Observe phase, qualitative research challenges internal beliefs with direct evidence from customers, patients, users or employees. The result is a ranked view of which beliefs carry the most risk and deserve testing first.
The firm's Test phase then validates the most consequential hypotheses through A/B tests, pilots and usability testing before they are scaled. Datalidy also works with leadership teams to build the habits that keep assumptions visible, including explicit decision criteria, structured review of evidence and a culture where questioning beliefs is safe. That combination of method and coaching helps organizations replace expensive guesses with knowledge they can build on, and to keep doing so as markets, products and teams change.
Frequently asked questions
How do we know which assumptions to test first?
Rank assumptions on two dimensions: how much the plan depends on the belief being true, and how little evidence currently supports it. Beliefs that are both critical and uncertain should be tested first. Also consider cost of testing, since some important assumptions can be checked quickly with existing data or a small number of interviews, which makes them easy early wins.
Isn't experience a valid basis for assumptions?
Experience is valuable and often correct, especially in stable conditions. The risk arises when conditions change, when experience comes from a different context, or when it concerns behavior that leaders do not observe directly. Testing an experienced judgment costs little if the judgment is right and saves a great deal if it is not. Good leaders welcome that check rather than resist it.
How can leaders encourage teams to challenge assumptions?
Leaders set the tone by labeling their own beliefs as hypotheses, asking what evidence would change their minds, and responding constructively when that evidence arrives. Psychological safety, as described by Amy Edmondson, matters here: people need to feel safe raising doubts without penalty. Simple routines, such as listing key assumptions in every major proposal, make questioning a normal part of decision-making.
