What are incorrect assumptions in a target group profile?

An audience may treat something as true when making a choice even though it is incorrect. For example, it may assume that two suppliers with the same overall score also have identical scores on every individual criterion. Such a premise can determine which information someone still examines and which differences go unnoticed.

The Incorrect assumptions field records these mistaken premises. It requires two kinds of support: evidence that the assumption occurs in the researched audience and a verifiable basis for calling it incorrect in the relevant context. The provider’s judgement alone is insufficient.

Incorrect assumptions are premises the researched audience treats as true when making a choice, although relevant, verifiable information shows that they are incorrect in the stated context. This is Neurofactor’s practical working definition for the profile field.

Describe the assumption, its scope and the basis for the correction. If you only know that an assumption has not been substantiated, call it unproven or a matter for investigation. Unproven is not the same as incorrect.

A personal preference, value or valid requirement does not become wrong because the provider would prefer a different choice. An unfavourable experience may also be accurately described while the general conclusion drawn from it goes too far.

The field primarily concerns the audience’s assumptions. The researcher’s assumptions about that audience belong in the research record as hypotheses. Keep those perspectives visibly separate.

Incorrect assumptions is a practical profile label here. The research approach draws on original experiments about hypothesis testing and judgement.

Wason used a number task to investigate how participants tested their proposed rule. Searching only for matching examples was insufficient to establish the correct rule. Wason (1960)

Lord, Lepper and Preston investigated considering opposite possibilities. In their experiments, this approach corrected judgement bias more than instructions simply to be unbiased. Lord et al. (1984)

These are not studies of this profile field or a guaranteed sales approach. Our practical interpretation is that researchers must also seek counterevidence and alternative explanations. Imagining an alternative possibility does not establish that it is true.

Check four elements before applying the label incorrect.

Pay attention to words such as always, never, everyone and only, but do not insert them yourself. A statement about one situation is not a universal claim. Nor does one exception automatically refute “this usually happens”; the underlying frequency needs appropriate investigation.

1. Start with the decision. Ask which information someone uses and what they infer from it. Record their role, situation and decision step.
2. Make the premise explicit. For example: “What does that overall score mean to you?” Check your summary without already suggesting a correct answer.
3. Define the statement’s scope. Does it concern a fact, likelihood, expectation, preference or rule? Which period, version or circumstances does the participant mean?
4. Choose an appropriate check. Use a relevant source, calculation, demonstration or research design. Check that the information addresses the same claim.
5. Seek information that could change your own judgement. What supports the assumption? Under which conditions might it be correct? Do not look only for evidence favourable to the offer.
6. Record the finding and its limits. Mark it as incorrect, overstated, context-dependent or not yet established. Record separately whether the participant understands the correction and what it means for the choice.

Testing an assumption and persuading an audience are different activities. More accurate knowledge need not lead to a purchase; other goals, conditions or preferences may remain decisive.

Retain the version and check date alongside sources. A statement about an earlier product version may have been correct at the time. A new version working differently does not establish that the original speaker was mistaken then.

Entirely fictional calculation; not customer data or measured supplier quality. Two suppliers receive scores on a scale from 0 to 10. The two criteria carry equal weight in this example.

The assumption to test is: “If the average is identical, the individual criterion scores are identical too.” The calculation shows that this general inference is incorrect. Different scores can produce the same average.

This does not establish which supplier is better. Goals, criteria, weights and any minimum requirements matter for that judgement. Nor does the calculation establish that a real audience holds this assumption.

A fictional profile sentence is: “Assumes that an equal average means identical criterion scores, although different score combinations can produce the same average.” In actual research, this sentence may be recorded as an audience characteristic only once its occurrence and decision relevance have been established.

An associative target group is a bounded group of people whose similar associations guide decisions within a specific choice context.

An association and a testable assumption are not identical. In a fictional example, a score overview may evoke “certainty”. That feeling does not establish that someone believes the scores are complete or error-free. Ask what meaning the person attaches to it and which inference they actually use.

Conversely, similar assumptions may have different backgrounds. Investigate the pattern and choice context before grouping people together. A misconception is not a fixed personality characteristic and does not yield a BIS/BAS score.

• Treating rejection as a misconception. An informed audience may reasonably find an offer unsuitable.
• Equating unproven with incorrect. Missing support warrants uncertainty, not automatically the opposite conclusion.
• Trying to refute a preference. “I value personal contact more” is not a factual error.
• Strengthening the statement beyond its original meaning. Do not turn “sometimes” into “always”.
• Using a bias as a diagnosis. Identify the testable assumption before supplying an explanation of the thinking process.
• Extending a correction too far. A counterexample to an absolute rule does not establish that the rule never holds.
• Exempting your own proposition from scrutiny. The provider’s supporting evidence requires the same care.

Use this working template: “Assumes in [context] that [testable statement], although [substantiated correction with relevant limits].”

In the linked record, retain the original statement, role, evidence of occurrence, support for the correction, conditions, decision relevance and check date. Keep evidence that someone believes something separate from evidence that the belief is incorrect.

Without sufficient information, write “No incorrect assumption established yet”. Possible assumptions remain hypotheses in the research record. Without a specific product, you can still investigate premises about the choice context, provided you do not invent product characteristics or audience beliefs.

A useful Incorrect assumptions field shows which premise occurs in the audience and why it is incorrect in the stated context. The correction remains verifiable and bounded. This supports clear information and research without dismissing preferences or unanswered questions as errors.