Analytical Errors in the Lab: Causes, Detection & Prevention

October 5, 2026

By Godavari Doke

Sr. Content Writer

Reviewed By Samuel Stone

Product Manager

TL;DR

Analytical errors in laboratory testing occur during the measurement process and can produce inaccurate patient results even when samples appear normal. Common causes include equipment malfunction, calibration drift, reagent issues, quality control failures, operator mistakes, and sample interference. Labs can detect and prevent these errors using Westgard rules, delta checks, proficiency testing, preventive maintenance, and automated result verification. A well-configured laboratory information system (LIS) helps flag suspicious results, hold results when controls fail, and maintain an audit trail—but identifying and correcting the root cause still requires human oversight.

Now for the plot twist. A 2025 study in Clinical Chemistry and Laboratory Medicine tracked 87,317 errors in a core laboratory that ran 37.7 million billable results. Pre-analytical errors made up 85,894 of them. Analytical errors? Just 451. That works out to roughly one analytical error for every 83,000 results.

Good news, right? Mostly, yes. It is also exactly why analytical errors deserve your attention. Nobody hunts for a problem that shows up once in 83,000 results. A mislabelled tube waves at you from the bench. A drifting analyser does not. The sample looks normal, the machine hums along, and a perfectly believable number lands in front of a clinician. That is how a single analytical error turns into a treatment decision.

So let’s walk through analytical errors in laboratory testing together: what they are, where they come from, how labs catch them with QC, delta checks and auto-verification, and which controls stop them coming back. We will also look at what your software should flag, because software can sound the alarm but it cannot fix the cause.

Analytical Errors Happen Between Sample Receipt and Result Release

The analytical phase starts when the specimen is ready for testing and ends when the analyser has produced a result and someone (or something) has checked it. Anything that goes wrong in that window, from a worn pipette tip to a skipped calibration, counts as an analytical error.

What the analytical phase includes

Think of it as six jobs in a row. The analyser processes the sample. Reagents react. Calibration turns raw signals into concentrations. QC runs prove the system behaves. The analyser measures. Then a first-level check decides whether the result can leave the bench. Each job has its own way of going wrong, and we will meet them all below. (Some textbooks write “analytic error”. Same thing, shorter word.)

How analytical errors differ from pre- and post-analytical errors

Phase	Typical errors	Where it is caught
Pre-analytical	Wrong tube, mislabelled sample, delayed transport, haemolysis at collection	Sample acceptance checks and rejection criteria
Analytical	Calibration drift, bad reagent, pipetting fault, skipped QC	QC rules, delta checks, auto-verification
Post-analytical	Transcription slip, wrong report destination, late critical call	Report review, interface checks, turnaround audits

The difference in one line: pre- and post-analytical errors happen around the test. Analytical errors happen inside it, and they hide because the sample looks fine. For the phases on either side, see the guides on pre-analytical errors and post-analytical errors. The laboratory errors pillar guide covers all three phases together.

Non-analytical errors, meaning the pre- and post-analytical kind, account for most of what goes wrong in a lab. That does not make the analytical share harmless. A pre-analytical slip usually hurts one patient. A systematic analytical error can hurt every patient in the run.

How big is the analytical share?

It depends on who is counting, but recent audits agree on one thing: the analytical slice is thin.

Share of confirmed laboratory errors by testing phase (%), 2025 and 2026 studies
Study	Pre-analytical	Analytical	Post-analytical
Clinical Chemistry and Laboratory Medicine, 2025, core laboratory (87,317 errors)	98.4	0.5	1.1
Same study, excluding haemolysis (26,569 errors)	94.6	1.7	3.7
Journal of Clinical Laboratory Analysis, 2025, chemistry laboratory in Ethiopia	75.9	1.8	22.3
International Journal for Quality in Health Care, 2026, six-hospital network in Thailand, voluntary incident reports (1,031 errors)	89.2	0.1	10.7

Read the rows with care. The core-laboratory team counted haemolysis as an error, and it alone caused 60,748 of the 87,317 errors (69.6%), which inflates the pre-analytical share. Take it out and the analytical share rises to 1.7%. It still stays small.

The Thai figure deserves a second look. It comes from voluntary incident reports across 5.95 million samples, so it counts only what staff noticed and chose to report. Analytical errors are the ones people notice least, so a fair reading is that 0.1% is a floor, not a fact. The Ethiopian study adds a twist: its analytical phase scored 3.5 sigma, the best of the three phases, yet still short of 4.

What Are the Main Types of Analytical Errors?

Analytical errors come in two main flavours: random and systematic. A third group, gross errors, covers one-off slips that fit neither pattern. The random error vs. systematic error split matters because each one calls for a different fix.

Aspect	Random error	Systematic error
Pattern	Scatter above and below the true value	Consistent shift in one direction
Effect on results	Lower precision, wider spread	Lower accuracy, results biased
Typical causes	Pipetting variation, unstable power or temperature, electrical noise	Calibration drift, reagent lot change, degraded reagent
Typical fix	Find the unstable component, then re-verify precision	Recalibrate or check the reagent lot, then re-verify bias

Random Error

Random error is unpredictable scatter around the true value. You see it as imprecision: run the same sample twice and the answers refuse to agree. The usual suspects are pipetting variation, unstable power or temperature, and electrical noise. The full list of causes lives in the laboratory errors pillar guide.

Systematic error (bias)

Systematic error is a consistent nudge in one direction. Picture a bathroom scale that always reads one kilo heavy. It is calm, consistent, and wrong, and it never argues with you. It comes in two forms.

Constant bias adds the same amount at every concentration. A result 5 mg/dL too high at 100 mg/dL is also 5 mg/dL too high at 400 mg/dL.
Proportional bias grows with concentration. A 5% error reads 105 at a true 100, and 420 at a true 400.

Typical sources are calibration drift, a reagent lot change, and degraded reagent. This is one reason labs run controls at more than one level: a single level can hide a bias that only shows at the high or low end.

Here is what both patterns look like on a control chart. Take a glucose control with a target mean of 100 mg/dL and a standard deviation (SD) of 2 mg/dL. Five runs return 97, 104, 99, 95, and 103. The values bounce above and below the mean, and one (95) lands 2.5 SDs low. That is random error. Now try five different runs: 103, 104, 103, 105, and 104. Every value sits above +1 SD (102 mg/dL). Nothing looks dramatic, yet the whole set has moved. That is systematic error.

Gross (procedural) errors

Gross errors are one-off slips: the wrong reagent loaded, a step skipped, the wrong dilution. They form no pattern, so statistical QC may never notice them. Good procedures, training, and result checks do.

Common Causes of Analytical Errors in the Clinical Laboratory

Analytical errors in clinical laboratory testing trace back to five places: equipment, reagents and calibration, quality control, people, and sample interference. Each has a typical failure and a matching control. The prevention section below covers the controls.

Equipment malfunction and drift

Equipment malfunction and drift cause errors when worn parts, clogged probes, or shifting temperature and optics change how the analyser measures. The change usually creeps in slowly, so results look plausible for days. Scheduled maintenance, with every action logged, is the control that catches it.

Reagent and calibration issues

Reagent and calibration issues create bias without any visible fault. A reagent error can be as simple as an expired vial, poor storage or a lot change. A calibration error can be a missed calibration, a failed one that someone accepted, or the wrong calibrator. Tracking lots and calibration status stops both.

Quality control failures

A QC failure is not only a control that falls out of range. It also covers controls nobody ran, controls run too rarely for the workload, and out-of-range controls ignored while patient results go out anyway. The fix is a QC plan with written stop rules that nobody can skip.

Operator and procedural error

Operator and procedural error covers skipped SOP steps, wrong dilutions, the wrong test programme, manual overrides, and plain fatigue at the end of a heavy shift. Misprinted or misread barcodes belong here too. A result matched to the wrong sample ID is a perfect number for the wrong patient (see the guide to barcode systems in the laboratory). Training, clear SOPs and barcode checks are the controls.

Interference and test-system limits

Interference and test-system limits include haemolysis, lipaemia and icterus, results beyond the linear range and carry-over from one sample to the next. These do not break the analyser; they fool it. Serum index checks, dilution rules and carry-over testing are the controls.

Put the five causes side by side, and a pattern jumps out. Almost none of them set off an alarm on their own. So the next sections are about building alarms.

Examples of Analytical Errors in the Lab

Six quick scenarios show what analytical errors in the lab look like on a normal Tuesday. They are illustrative composites, not real customer cases.

Scenario	Error type	How it is detected	How it is prevented
New reagent lot arrives and control values shift upward	Systematic	Westgard rules on the control chart (4-1s, 10x)	Lot-to-lot comparison before the new lot goes live
Calibration skipped after analyser maintenance	Systematic	QC shift after maintenance; calibration log review	Checklist that links maintenance to calibration and QC
Wrong dilution on a very high result	Gross	Delta check or implausible-value flag	Written dilution rules; automatic rerun rules
Controls out of range, results released anyway	Gross	Audit of QC records against release times	System hold that blocks results when QC fails
Analyser drifts across a shift	Systematic	Trend in control means; QC at set intervals	Risk-based QC frequency; preventive maintenance
Analyser result mapped to the wrong test code in the LIS	Systematic	Reconciliation of analyser output against LIS values	Interface validation at go-live and after every change

Notice what is missing from every row: a dramatic mistake. Nobody did anything outrageous. The process simply had no step that said stop.

A real case: the vitamin that fooled the analyser

Composites are handy, but a real case makes the point harder. In November 2017, the US FDA warned that biotin, the vitamin sold for hair, skin and nails, can distort immunoassays that use biotin-streptavidin chemistry. Depending on the test, it can push results falsely high or falsely low. The FDA reported a patient death linked to a falsely low cardiac troponin result caused by biotin interference. Hair and nail supplements can contain up to 20 mg of biotin, against a recommended daily intake of 0.03 mg (TCTMD).

Look at where the error lived. The analyser did not malfunction. The interference sat in the patient’s sample, and the only alarm was a result that did not match the patient in front of the doctor. That is the case for interference limits in your SOPs, for reasonableness checks, and for telling clinicians to ask about supplements when a number looks wrong. The case dates from 2017, but the lesson has not aged a day.

How Labs Detect Analytical Errors Before Results Go Out

Detection works in layers: statistical QC on controls, checks on each patient result, and comparison with other labs. No single layer catches everything, which is why labs stack them. Five methods do most of the work:

Westgard rules catch random and systematic shifts in control results.
Delta checks catch a patient result that jumps unexpectedly from the last one.
Proficiency testing catches bias that a lab cannot see from the inside.
Calibration verification and trend monitoring catch slow drift between calibrations.
Auto-verification holds results that break a rule and releases the rest.

Internal quality control and Westgard rules

Internal quality control (IQC) means running control materials with known values and plotting the results on a Levey-Jennings chart. The chart tracks each control against the mean and the limits at 1, 2, and 3 SD. Westgard rules then tell you what the pattern means (Bio-Rad Unity documentation). The first rule only warns. The rest reject the run.

Westgard multirules: what each one flags
Rule	What triggers it	Points to	Action
1-2s	One control beyond 2 SD	Warning only	Check the other rules
1-3s	One control beyond 3 SD	Random error, or a large shift starting	Reject the run
R-4s	Two controls in one run differ by more than 4 SD	Random error	Reject the run
2-2s	Two consecutive controls beyond the same 2 SD limit	Systematic error	Reject the run
4-1s	Four consecutive controls beyond the same 1 SD limit	Systematic error	Reject the run
10x	Ten consecutive controls on one side of the mean	Systematic error	Reject the run

Labs choose their rule set from their own QC design, so follow your lab’s QC plan. For a quick overview of common error sources, the earlier guide on common analytical mistakes and how to solve them makes a good companion.

External quality assessment (proficiency testing)

External quality assessment (EQA), also called proficiency testing, sends your lab unknown samples from a provider and compares your results with those of peer labs. It exposes bias that internal controls can miss, because the controls and the method can drift together. A failed EQA round should start an investigation, not a shrug. ISO 15189:2022 covers EQA in clause 7.3.7.3, as part of ensuring valid examination results, and expects providers to meet ISO/IEC 17043 (ASLM).

Calibration verification and trend monitoring

Calibration verification confirms that the measuring system still gives accurate results across its measuring interval. Trend monitoring watches control means over days and across reagent lots, so you spot a slow slide before it crosses a limit. Between calibrations, drift is the thing to watch.

Result verification and auto-verification

Result verification is the last look before a result leaves the lab. Auto-verification automates most of that look. The system applies rules for QC status, analyser flags, delta checks, critical values, and measuring range. Results that pass go out automatically; the rest wait for a person. CLSI guidelines AUTO10-A and AUTO15 explain how to build and validate those rules.

Recent studies show what this can look like. A 2024 HbA1c study at Peking Union Medical College Hospital passed 83.7% of results automatically and cut average turnaround by 121.1 minutes. A 2026 thyroid panel study at the same hospital passed 75.2% on the first run and cut median turnaround from 122.1 to 88.6 minutes. Different labs use different rules, so treat these as examples, not benchmarks. And name a reviewer for held results, or they just pile up.

How Can Labs Prevent Analytical Errors?

Labs prevent analytical errors by combining scheduled maintenance and calibration, a risk-based QC plan, trained operators with clear SOPs, and documented corrective action. Each control needs a named owner and a record, because an unowned control is only a suggestion.

Detection-and-prevention controls by cause (owners are examples; assign by your own structure)
Cause	Detected by	Prevented by	Owner	Record kept
Equipment malfunction and drift	Trend in control means; QC shifts	Preventive maintenance schedule	Bench lead or biomedical engineer	Maintenance log
Reagent and calibration issues	Calibration verification; new-lot comparison	Lot tracking, storage checks, calibration schedule	Section supervisor	Lot and calibration records
QC failures	Westgard rules; QC record audit	Risk-based QC plan with stop rules	Quality manager	QC charts and review sign-offs
Operator and procedural error	Delta checks; review of held results	Training, competency checks, SOPs	Lab manager	Training and competency records
Interference and test-system limits	Serum indices; analyser flags; linearity rules	Dilution rules and interference limits in the SOP	Technical lead	Method verification file

Schedule preventive maintenance and calibration

Set intervals by analyser and workload, not by habit. A high-volume chemistry analyser running round the clock needs a tighter schedule than a benchtop unit that runs twice a day. Log every action, including the boring ones. The log is what you show an assessor.

Define a risk-based QC plan

Set QC frequency by workload and by how stable each analyte is. Then write down what happens when QC fails: stop reporting, investigate, correct, rerun controls, and review results released since the last good run. CLSI’s EP23 guideline on risk-based QC is a sound starting point.

Train operators and write SOPs that match the bench

Run competency checks at hire and at regular intervals. Then read your SOPs against what really happens at the bench. People skip steps that make no sense, so either fix the step or fix the training, and know which one you are fixing.

Document corrective action and review

Log every nonconformity, find the root cause, fix it, and later check that the fix worked. ISO 15189 expects that loop from a medical laboratory’s management system. It also turns one bad batch into a lesson instead of a pattern.

What Lab Software Should Flag: QC, Result Verification and Audit Trail

Software cannot remove analytical errors, but it can stop a flagged result from reaching a report and keep the evidence that it did. That is a smaller job than “fixing quality” and a bigger one than it sounds.

Why this matters more in 2026

Three shifts are raising the bar.

First, the transition clock ran out. ILAC set 6 December 2025 as the deadline for accredited medical laboratories to move to ISO 15189:2022 (ENAC). The 2022 edition groups internal QC, EQA, and result comparability under one heading: ensuring the validity of examination results. Proving that your results stay valid now sits in one place.

Second, patient-based real-time QC (PBRTQC) is gaining ground. It watches patient results for drift between control runs. A 2025 review by Dean, Smith, and Badrick says adoption is still slow, mainly because labs underestimate the tuning it needs. The same review adds that accreditors can accept PBRTQC under ISO 15189 and CAP (Journal of Laboratory and Precision Medicine).

Third, AI is reaching the bench. In February 2026, the IFCC’s committee on AI published best-practice recommendations. Verify any AI system locally, even one with regulatory approval, and watch for data drift (Clinical Chemistry and Laboratory Medicine). Sound familiar? It is QC thinking applied to algorithms: trust nothing until you test it, then keep testing.

A checklist for your laboratory information system

A laboratory information system should:

Validate results with rules, so clean results flow through and anything unusual waits for a person
Capture data straight from analysers through instrument interfaces, which removes manual transcription
Show QC trends, including Levey-Jennings charts, for every analyser, department and site on one screen
Flag QC rule violations, and block or hold patient results when controls fail
Run delta checks and flag critical or implausible values
Route critical values to a named person straight away
Track reagent lots and calibration status against each run
Log every user action and result change (who, what, and when), and limit access by role

Notice what is missing from that list: finding the cause. A hold tells a person to look. The person still has to find the clogged probe.

Try it this week. Print the checklist, mark your own system line by line as yes, partly, or no, and take the gaps to your next vendor or IT review. The “partly” lines usually hide the real risk.

Where Analytical Errors Fit in the Full Laboratory Error Lifecycle

Analytical errors are one of three phases, and a result is only as good as its weakest phase. A flawless analyser cannot rescue a mislabelled tube. A perfect tube cannot rescue a result someone mistypes at the end. The controls connect: pre-analytical checks protect what goes onto the analyser, analytical controls protect the number that comes out and post-analytical checks protect how that number reaches the clinician.

Read the three phases together. Start with the laboratory errors pillar guide, then go deeper on pre-analytical errors and post-analytical errors.

Frequently Asked Questions

What is the difference between analytical and pre-analytical errors?

Analytical errors happen on the analyser, during measurement. Pre-analytical errors happen before testing, during ordering, collection, labelling and transport, and they make up the largest share of laboratory errors. Analytical errors are rarer but harder to see because the sample looks normal. See the guides on pre-analytical errors and post-analytical errors.

What share of laboratory errors are analytical?

Analytical errors are a small share of laboratory errors. A 2025 core-laboratory study found 0.5% (1.7% once it excluded haemolysis), and a 2025 chemistry-laboratory study in Ethiopia found 1.8%. Labs count errors differently, so the exact figure varies. The share is small, but the errors are harder to see.

Which QC rule violations point to systematic error and which to random error?

Under Westgard rules, 1-3s and R-4s point to random error, while 2-2s, 4-1s, and 10x point to systematic error. The 1-2s rule only warns you to check the other rules. A 1-3s violation can also signal the start of a large systematic shift, so investigate every rejection. See the detection section above.

What should a laboratory do when QC fails?

A laboratory should stop reporting the affected tests, investigate the cause, correct it, and rerun the controls. Then it should review patient results released since the last acceptable QC run and document every step. Reporting resumes only after the controls pass. See the prevention section for the full plan.

Can a laboratory information system prevent analytical errors?

A laboratory information system cannot remove the cause of an analytical error, but it can flag one. It can hold results when QC fails, apply delta checks and auto-verification rules, and keep an audit trail. People still have to find and fix the root cause. See the software checklist above for what it should flag.

Conclusion: Software Flags, People Fix

Analytical errors split into random and systematic, with the odd gross slip on top. Most trace back to five causes: equipment, reagents and calibration, QC, people, and interference. Detection works in layers, prevention needs owners and records, and software flags and holds while people fix. Analytical errors are rare, quiet, and expensive. The labs that stay out of trouble are not the ones with perfect analysers. They are the ones where a bad number has nowhere to hide.

Ready to put this into practice? Run the software checklist against your own system this week, then read the Laboratory Errors pillar guide for the full picture across all three phases. Up next: what happens after the number exists, in the post-analytical errors guide.