Debrief: what these numbers support, and what they do not

Supported by this dataset: a mean, sample SD, CV, and bias with correct units for a defined, stable run, and a standardized distance describing how far one result sits from that run's mean in SD units. Also supported: recognizing that a steady directional climb across ten days is a different finding from a scattered high point in a five-result run, and that neither pattern is resolved by deleting the values that make the statistics look worse.

Not supported: any pass or fail judgment. This teaching case declared no laboratory acceptance limits and no QC rule set, so nothing here authorizes releasing or holding patient results in a real laboratory; that authority comes from local policy, not from a mean and an SD. Also not supported: treating five or even ten results as proof of long-term stability. A small dataset gives an imprecise estimate of the true mean and SD, and normality is an assumption to check, not a property guaranteed by any dataset size.

What depends on method and local policy: whether the SD used in a middleware or spreadsheet is sample or population, the number of observations required before assigning or revising a control mean and SD, the acceptance limits and QC rule set, who may review or override a flagged event, and the exact definition of a control lot's data window, including handling for a lot change.

Statistical QC on a control material helps detect assignable-cause shifts in the analytical process, but it does not detect every individual-specimen error: specimen-specific interference, carryover, and preexamination error can all affect a single patient result without moving a control at all. Statistics on control data and review of an individual patient result are related but separate checks, and one does not substitute for the other.

A mean, SD, CV, and bias tell you what a defined dataset looked like; whether that is acceptable, and what to do about it, is always a separate, locally defined decision.