Bias and Evidence Quality Review
Bias and Evidence Quality Review helps the founder or programme team critically review confirmation bias, weak questions, unrepresentative evidence and false positives. Within Customer Discovery, Validation & Evidence, it turns a broad or uncertain area of the venture into a concrete Bertie work product that can be reviewed, improved and reused. The task is intentionally discrete: it should produce a specific artefact, decision, evidence item or risk signal rather than general learning notes.
Critically test the quality, consistency and readiness of Bias and Evidence Quality Review. The objective is to remove ambiguity around confirmation bias, weak questions, unrepresentative evidence and false positives, give the founder a decision-ready output, and make it clear whether the venture should progress, repeat the task with stronger evidence, escalate to expert support, or move into a linked stage.
Bertie or a programme manager assigns Bias and Evidence Quality Review when the venture needs a decision-ready output for this group. Typical triggers include group-gate reviews, evidence gaps identified by the co-pilot or founder request.
problem hypothesis; target customer assumptions; existing interviews or evidence; current alternatives; evidence log; existing artefact to review; specific context for confirmation bias, weak questions, unrepresentative evidence and false positives.
The founder consolidates all raw interview transcripts, survey responses, notes, and logbooks gathered during customer discovery. They map these primary data points directly against the specific hypotheses and decisions they intend to validate.
ObjectiveCompleting this action compiles a complete, unedited repository of primary customer discovery data. It establishes a transparent baseline for audit, ensuring subsequent bias evaluations rely on verifiable customer artefacts rather than selective memory.
What's expectedThe founder must present an organised data-room repository containing all raw notes, recorded interviews, respondent metadata, and the specific claims derived from them. Every summary claim within the venture documentation must be directly traceable to an unedited raw data source.
Open action arrow_forwardConsultant stress-test · 5 questions- 1.What proportion of your total interview transcripts have been included in this repository without summary or cherry-picking?
- 2.How have you ensured that verbal tones, pauses, and non-verbal signals are preserved alongside written notes?
- 3.Which specific customer statements directly support the core problem claim, and where are the corresponding raw logs stored?
- 4.Why have certain customer interactions or partial responses been excluded from this audit file?
- 5.How does the completeness of this dataset compare to the total number of prospect engagements attempted?
- A data-room asset titled Bias and Evidence Quality Review
- A review note with a verdict, evidence gaps, risks, recommended corrections and a clear continue / repeat / escalate decision
- It should update the venture DNA with specific evidence or decisions about confirmation bias, weak questions, unrepresentative evidence and false positives, create a visible milestone in the founder journey, and generate one or more recommended next tasks
Bertie co-pilot turns interview notes and customer signals into tagged evidence, detects confirmation bias, scores evidence quality, and recommends validation or MVP tasks. For this task, it should focus on confirmation bias, weak questions, unrepresentative evidence and false positives, prompt the founder for missing inputs, draft or improve the output, flag weak assumptions, and record the result back into the relevant data-room section.
A mentor or evaluator can review the output at the group gate. Programme managers can require an advisor checkpoint before Bertie moves the venture forward.