The visible slice
A review queue shows the transactions your current controls selected. Learning only from that queue can reveal precision among alerts, but it cannot establish how much harmful activity exists outside it. A quiet dashboard may reflect low risk, low coverage or simply limited observation.
Choose a population
Define the population and period you want to understand before selecting records. A probability sample can support population inference when its design and outcome process are suitable. A handpicked collection of interesting cases is useful for investigation, but it answers a different question from a representative sample.
A balanced audit
A fictional team samples approved transactions across three product segments, deliberately examining more records from a small segment. This can improve learning about that segment, but a pooled result needs appropriate weighting to estimate the overall population. The raw sample mix no longer mirrors the traffic mix.
Respect uncertainty
Record selection probabilities, unavailable outcomes and the time allowed for evidence to develop. Small samples can miss rare events, so zero observed incidents is not proof that none exist. Keep audit selection distinct from normal customer decisioning, and review any proposed experiment’s customer impact before implementation.