Common mistakes in brand tracking
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerResearch Wiki
Research mistakes and myths
Explanations of common research failure modes, from oversized confidence in NPS and sample size to synthetic respondents and statistical significance.
Why NPS can be misleading
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy high CSAT does not guarantee loyalty
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy purchase intent overstates actual demand
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy consumers cannot always explain their behaviour
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy larger samples do not automatically mean better research
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy online samples are not automatically representative
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy statistical significance may be commercially irrelevant
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy correlation does not establish causality
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy cluster solutions can be unstable
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy segmentation often fails after delivery
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy conjoint results can be unrealistic
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy pricing research produces contradictory answers
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy social sentiment is not public opinion
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy AI-generated insights can sound right and still be wrong
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy synthetic respondents cannot fully replace primary research
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy poor recruitment ruins good methodology
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy city-level conclusions from small samples are dangerous
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy questionnaire length damages data quality
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy respondent incentives can introduce bias
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy weighted data may have a smaller effective sample
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
Open explainerWhy “pan-India coverage” does not guarantee field quality
A diagnostic explainer separating the observed result from the inference teams are tempted to make.
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