Accuracy
Sets your reported value against the assigned or consensus value for the sample and shows the deviation directly.
Use it to: spot a systematic offset and decide whether a calibration adjustment is warranted.
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What you receiveSeven reports that turn
a number into an action
Each cycle produces a set of statistical views of your performance. Every one is built to answer a specific question your quality manager will be asked in an audit.
Precision
Assesses how consistent your results are across repeated samples in the cycle, expressed as a coefficient of variation.
Use it to: separate scatter from bias — poor precision points at maintenance, not calibration.
Bias
Quantifies systematic error as a percentage difference between your results and the peer or reference value.
Use it to: size the correction needed, and check it against your allowable total error.
Z-score
Standardises your result as the number of peer standard deviations from the consensus mean, so analytes on different scales compare directly.
Use it to: triage the month — which analytes are fine, which need a look, which need action.
Outlier summary
Flags results falling outside the expected range for the peer group, with a histogram showing where you sit in the distribution.
Use it to: start a root-cause investigation with the evidence already assembled.
Trend analysis
Plots accuracy and precision across the cycle so slow drift becomes visible long before any single month breaches a limit.
Use it to: schedule recalibration on evidence rather than on the calendar.
End-of-cycle summary
Consolidates accuracy, precision, bias and outliers for every analyte across all twelve months, with your participation record.
Use it to: submit with a NABL application or hand to an assessor at surveillance.
Interpreting your report
Read your Z-score
A Z-score states how many peer-group standard deviations separate your result from the consensus. Put last month's numbers in — it works with any provider's report, not only ours.
+1.56
Within ±2 SD the result is acceptable. Between 2 and 3 SD it is a warning — fine for the month, but check whether recent months sit on the same side of the mean. Beyond 3 SD it is unacceptable and needs a documented corrective action.
How the numbers are produced
Peer grouping is what makes the comparison fair
Comparing an enzymatic creatinine result against a Jaffe consensus produces a bias that says nothing about your laboratory. So the analysis groups participants by the method and instrument each declared at enrolment, trims outliers before establishing the consensus, then calculates your statistics against that group.
Where a peer group is too small to produce a meaningful consensus, your result is assessed against the all-method group and the report states that plainly, so you can weigh the bias correctly rather than chase a phantom.
Every calculation runs the moment a cycle closes. Nothing is transcribed by hand between your submission and your report. Results submitted through the participant portal feed the statistics engine directly. Reports publish to your account within days of the closing date.
Enrolment for the 2026 cycle is open
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