Random Sampling and Review
Drawing a random sample of cases, examining each one in depth, and using the findings to estimate how often a problem occurs, why, and how much it costs across the whole population.
No AIestablishedstatistical
Random sampling and review is the measurement that tells an organisation how large a problem is and where it comes from.
It belongs before any targeted or predictive method, both because it shows where effort should go and because it is the only fair test of whether a targeted method is finding more than chance would.
General strengths
- Gives an unbiased estimate of a rate and its causes, which no targeted review can, because nobody chose which cases to look at.
- Produces labels that are free of the selection bias in cases chosen by suspicion or by an earlier model.
- Provides the baseline against which any targeted method has to prove that it finds more than chance.
General limitations
- Each in-depth review is expensive, so the sample is small and estimates for small groups are imprecise.
- Finds problems after they have happened, so it measures and directs prevention rather than preventing anything itself.
- Depends on the reviewers applying the same standard to every case, which takes training and quality checks.
Applied to business problems
Each of these is written for its own problem. The technique is shared, the solution is not.