Troubleshooting High Measurement Uncertainty

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-27

Quick Answer

• High measurement uncertainty in laboratory assays typically stems from a small number of dominant components, so identify those first through Pareto analysis before making changes.
• Begin troubleshooting by documenting the uncertainty budget, then rank each component by its contribution to total uncertainty to target the largest sources.
• Reducing uncertainty requires iterative changes to calibration, environmental control, and operator technique, but some uncertainty is irreducible and must be accepted as inherent to the method.

At a Glance

Understanding Measurement Uncertainty in Laboratory Diagnostics

Measurement uncertainty describes the range within which the true value of a measured quantity is expected to lie. In laboratory diagnostics, every result carries some degree of uncertainty because instruments, reagents, operators, and environmental conditions introduce variability. A reported result without an associated uncertainty estimate is incomplete, because the laboratory and the clinician cannot judge whether the result is fit for its intended purpose.

The concept of uncertainty differs from error. Error is the difference between a measured value and the true value, and it can be corrected when identified. Uncertainty is a parameter that characterizes the dispersion of values that could reasonably be attributed to the measurand. It is not a mistake but a property of the measurement process itself. Laboratories that fail to estimate uncertainty may produce results that appear precise but are not accurate, or results that are accurate but not reproducible.

High measurement uncertainty affects diagnostic decisions. A result near a clinical decision threshold may be interpreted differently if the uncertainty range crosses that threshold. For example, a quantitative assay result of 10.0 units with an uncertainty of plus or minus 1.5 units may fall below or above a cutoff of 10.5 units depending on the actual value. The laboratory must therefore communicate uncertainty in a way that supports correct interpretation.

The National Institute of Standards and Technology provides the foundational framework for measurement science, including calibration standards and quality practices that laboratories use to establish traceability and reduce uncertainty. Laboratories that align their practices with these measurement science principles can systematically identify and reduce dominant uncertainty components.

Core Principles of Uncertainty Estimation

The Measurement Model

Every laboratory measurement can be expressed as a mathematical model that relates the output result to the input quantities. The model includes the measured signal, the calibration factor, the sample volume, the reagent concentration, and any correction factors. Each input quantity has its own uncertainty, and these uncertainties propagate through the model to produce the combined uncertainty of the final result.

The combined uncertainty is calculated by combining the individual uncertainty components. The standard method uses the law of propagation of uncertainty, which accounts for the sensitivity of the result to each input. Components that have a large sensitivity coefficient contribute more to the total uncertainty, even if their absolute uncertainty is small.

Type A and Type B Evaluations

Uncertainty components are evaluated in two ways. Type A evaluation uses statistical analysis of repeated measurements. For example, the standard deviation of replicate measurements of a control sample provides a Type A estimate of random variation. Type B evaluation uses other information, such as calibration certificates, manufacturer specifications, and prior experience. Both types are valid, and a complete uncertainty budget includes both.

Type A evaluations are straightforward for random effects, but they do not capture systematic effects. Type B evaluations capture systematic effects, such as the bias of a pipette or the drift of an instrument, but they rely on external information that may be incomplete. A robust uncertainty estimate requires both types.

Traceability and Calibration

Traceability is the property of a measurement result that links it to a stated reference through an unbroken chain of calibrations. The National Institute of Standards and Technology maintains reference standards and calibration services that provide the foundation for traceability in the United States. Laboratories that use certified reference materials and calibrated instruments can trace their results to these national standards.

Calibration is the process of establishing the relationship between the instrument response and the known value of a standard. A calibration that is performed with a certified reference material reduces the systematic error of the instrument. However, calibration itself has uncertainty, and that uncertainty must be included in the total uncertainty budget.

The Troubleshooting Workflow

Step 1: Document the Current Uncertainty Budget

The first step in troubleshooting high measurement uncertainty is to document the current uncertainty budget. This budget lists every input quantity, its uncertainty, and its sensitivity coefficient. The budget must be complete, because an incomplete budget will underestimate the total uncertainty and misdirect the troubleshooting effort.

Create a table with columns for the input quantity, the uncertainty source, the type of evaluation, the standard uncertainty, the sensitivity coefficient, and the contribution to the combined uncertainty. This table is the working document for the entire troubleshooting process.

Step 2: Perform a Pareto Analysis

The Pareto principle states that a small number of causes account for most of the effect. In uncertainty terms, a few components will dominate the total uncertainty. Calculate the contribution of each component to the combined uncertainty and rank them from largest to smallest. The top two or three components will typically account for most of the total.

Pareto analysis directs the troubleshooting effort to the components that matter most. Improving a component that contributes 1 percent of the total uncertainty will have a negligible effect, while improving a component that contributes 60 percent will have a large effect.

Step 3: Investigate the Dominant Components

For each dominant component, investigate the specific cause. The investigation should follow a structured approach that examines the instrument, the operator, the reagent, the environment, and the procedure.

Instrument Investigation

Check the calibration status of the instrument. A calibration that is expired or performed with a noncertified standard will produce a large systematic uncertainty. Verify that the calibration certificate is current and that the calibration was performed by a qualified provider. Check the maintenance log for recent repairs or adjustments that may have changed the instrument response.

Operator Investigation

Operator technique is a common source of high uncertainty, especially in manual steps such as pipetting, mixing, and extraction. Observe the operator performing the procedure and compare the technique to the written protocol. Check for common errors such as incorrect pipetting angle, incomplete mixing, or inconsistent timing.

Reagent Investigation

Reagents can introduce uncertainty through lot-to-lot variation, degradation, or improper storage. Check the expiration dates and the storage conditions. If a new reagent lot has been introduced, compare its performance to the previous lot using parallel measurements.

Method Investigation

The method itself may have inherent uncertainty. Some methods are more reproducible than others. Check the method validation data to see if the uncertainty is consistent with the expected performance. If the method has a known limitation, such as sensitivity to temperature or pH, the uncertainty may be unavoidable.

Step 4: Implement Corrective Actions

Once the dominant components are identified, implement corrective actions. The actions should be targeted to the specific cause. For calibration errors, renew the calibration with a certified reference material. For operator errors, provide refresher training and standardize the technique. For reagent issues, qualify the new lot or adjust the storage conditions.

Each corrective action should be documented, and the effect on the uncertainty budget should be evaluated. The evaluation requires repeating the uncertainty estimation after the action is implemented.

Step 5: Verify the Improvement

After implementing corrective actions, verify that the uncertainty has decreased. Repeat the uncertainty budget calculation and compare the new total to the previous total. The verification should include a check that the dominant components have been reduced and that no new components have become dominant.

Practical Implementation Steps

Create a Standard Operating Procedure for Uncertainty Estimation

A standard operating procedure for uncertainty estimation ensures that the process is consistent and reproducible. The procedure should define the scope, the responsibilities, the method for calculating the uncertainty budget, and the frequency of review. The procedure should also define the criteria for when the uncertainty is acceptable and when it requires action.

Train Personnel

All personnel who perform measurements or interpret results must understand the concept of uncertainty. Training should cover the basic principles, the calculation method, and the interpretation of uncertainty in the context of diagnostic decisions. Personnel should be able to identify when the uncertainty is high and when to escalate the issue.

Use Control Charts

Control charts are a practical tool for monitoring the uncertainty over time. Plot the control values for each run and calculate the mean and standard deviation. A control value that falls outside the control limits indicates that the uncertainty has increased and that the cause must be investigated.

Review the Uncertainty Budget Regularly

The uncertainty budget is not a static document. It must be reviewed regularly, especially when the method, instrument, reagent, or operator changes. A change in any of these factors can alter the uncertainty budget and require a new analysis.

Records and Measurements

What to Record

The laboratory must record the following information for each uncertainty analysis:

• The date of the analysis
• The instrument and its calibration status
• The reagent lots and expiration dates
• The operator who performed the measurements
• The environmental conditions, including temperature and humidity
• The raw data and the calculated uncertainty components
• The combined uncertainty and the expanded uncertainty

How to Record

The records must be clear and complete. Use a standardized form or a laboratory information management system. The records must be traceable to the specific run and the specific instrument. The records must be retained for the period required by the laboratory quality system.

How to Use the Records

The records are used to identify trends and to investigate problems. If the uncertainty increases over time, the records can be used to identify the cause. For example, if the temperature records show a gradual increase, the temperature control system may be failing.

Common Failure Patterns

Failure Pattern 1: Ignoring the Uncertainty Budget

The most common failure is not creating an uncertainty budget at all. Without a budget, the laboratory cannot identify the dominant components and cannot target the corrective actions. The result is a trial-and-error approach that wastes time and resources.

Failure Pattern 2: Focusing on the Wrong Component

A laboratory may focus on a component that is easy to measure but not the dominant component. For example, a laboratory may spend time improving the pipetting technique when the dominant component is the calibration of the instrument. The Pareto analysis prevents this failure.

Failure Pattern 3: Not Verifying the Improvement

A laboratory may implement a corrective action but not verify that the uncertainty has decreased. The action may not have the expected effect, or it may introduce a new source of uncertainty. The verification step is essential.

Failure Pattern 4: Ignoring the Environmental Conditions

Temperature and humidity are common sources of uncertainty. A laboratory that does not monitor the environmental conditions may have a high uncertainty that is not traced to the cause. The environmental conditions must be recorded and controlled.

Failure Pattern 5: Not Involving the Operator

The operator is a key source of uncertainty. A laboratory that does not involve the operator in the troubleshooting process may miss the operator-related causes. The operator should be part of the investigation and the corrective action.

Limitations and Interpretation

The Uncertainty Is Not the Error

The uncertainty is not the error. The uncertainty is a range of which the true value is expected to lie. The error is the difference between the measured value and the true value. A measurement can have a small uncertainty but a large error if the calibration is biased. The uncertainty budget must include the systematic error components.

The Uncertainty Is Not the Precision

The uncertainty is not the same as the precision. The precision is the reproducibility of the measurement, and it is a component of the uncertainty. The uncertainty also includes the systematic error. A measurement can be precise but not accurate, and the uncertainty will be high.

The Uncertainty Is Not the Tolerance

The uncertainty is not the tolerance. The tolerance is the acceptable range of the result, and it is defined by the clinical or regulatory requirement. The uncertainty is the range of the true value. A result can be within the tolerance but have a high uncertainty, and the result may not be reliable.

The Uncertainty Is Not the Standard Deviation

The standard deviation is a measure of the random variation, and it is a component of the uncertainty. The uncertainty also includes the systematic error. The standard deviation alone does not provide the full uncertainty.

Safety and Regulatory Context

Biosafety Principles

Laboratory work with biological samples requires adherence to biosafety principles. The CDC and NIH provide the Biosafety in Microbiological and Biomedical Laboratories, which defines the containment levels and the practices for handling biological materials. The uncertainty of the measurement does not change the biosafety requirements, but the laboratory must ensure that the uncertainty does not lead to a misinterpretation of the result that could affect the safety of the personnel or the environment.

Institutional Oversight

The NIH Guidelines for Recombinant or Synthetic Nucleic Acid Molecules provide the oversight for research involving recombinant or synthetic nucleic acids. The laboratory that works with these materials must follow the guidelines, including the risk assessment and the containment requirements. The uncertainty of the measurement does not change the oversight, but the laboratory must ensure that the uncertainty is not so high that the result is unreliable for the risk assessment.

The Role of the Laboratory

The laboratory has a responsibility to provide reliable results. The uncertainty is a key component of the reliability. The laboratory must ensure that the uncertainty is estimated and reported, and that the uncertainty is low enough for the intended use of the result.

Professional Escalation Criteria

When to Escalate

The laboratory should escalate the issue to a higher level when the uncertainty cannot be reduced by the corrective actions. The escalation criteria include:

• The uncertainty remains high after the corrective actions
• The uncertainty is high for a method that is critical for the diagnostic decision
• The uncertainty is high for a method that is used for regulatory compliance
• The uncertainty is high for a method that is used for a clinical trial

How to Escalate

The escalation should be documented and communicated to the laboratory director or the quality manager. The escalation should include the uncertainty budget, the corrective actions, and the results of the verification. The escalation should be done in a timely manner to avoid the delay in the diagnostic decision.

The Role of the Quality Manager

The quality manager is responsible for the quality system and the uncertainty. The quality manager should review the escalation and decide on the next steps. The quality manager may decide to change the method, the instrument, or the reagent. The quality manager may also decide to report the uncertainty to the clinician or the regulatory body.

Decision Framework for Prioritizing Uncertainty Reduction Actions

A Pareto analysis identifies which uncertainty components dominate the total budget, but it does not by itself tell you which components you should reduce first. Laboratories often waste resources by targeting the largest component when a smaller component is far easier and less expensive to fix. A decision framework that weighs the size of each component against the feasibility, cost, and risk of reducing it provides a more practical path to lowering total uncertainty.

The Effort Impact Matrix

The effort impact matrix is a structured way to rank uncertainty reduction actions based on two dimensions: the potential impact on total uncertainty and the practical effort required to achieve the reduction. Impact is measured by the component contribution to the combined uncertainty. Effort includes the time, cost, training, and disruption to routine operations.

The matrix directs attention to high contribution components that require low effort. These are the quick wins that produce measurable improvement without major disruption. A component that contributes 40 percent of the total uncertainty and can be fixed by recalibrating a pipette is a better first target than a component that contributes 50 percent but requires replacing the instrument.

Scoring the Reduction Actions

To use the matrix consistently, score each candidate action on a simple scale. For impact, use the component contribution from the uncertainty budget. For effort, score the action from 1 to 5 across four dimensions:

• Time to implement, from hours to months
• Direct cost of materials, calibration, or service
• Personnel training or behavior change required
• Risk of disrupting routine testing during implementation

Add the four effort scores to produce a total effort score from 4 to 20. Plot the component contribution against the effort score. Actions in the high contribution, low effort quadrant are the first priority. Actions in the high contribution, high effort quadrant require a cost benefit analysis before commitment.

The Cost Benefit Check

For high effort actions, perform a simple cost benefit check before implementation. Estimate the expected reduction in total uncertainty and compare it to the cost of the action. If the action reduces the expanded uncertainty from 15 percent to 8 percent and the method is used for a regulatory compliance test, the benefit may justify the cost. If the same reduction applies to a screening test where the result is confirmed by a second method, the benefit may not justify the cost.

The cost benefit check must also consider the consequence of not reducing the uncertainty. A method that produces results near a clinical decision threshold carries more risk than a method with results far from any threshold. The risk of misclassification is a cost that must be included in the analysis.

The Component Reduction Sequence

Once the decision matrix identifies the priority actions, apply a reduction sequence to each dominant component. The sequence moves from the simplest intervention to the most complex, and it verifies the effect at each stage.

Stage 1: Verify the Current State

Before changing anything, confirm that the uncertainty component is what you think it is. Recalculate the component contribution using the most recent data. Check the calibration certificate, the reagent lot records, and the environmental logs. A component that was dominant six months ago may no longer be dominant after a change in the instrument or the operator.

Stage 2: Apply the Least Invasive Correction

The least invasive correction is the action that does not change the method, the instrument, or the operator. For a calibration component, this may be verifying that the calibration certificate is current and that the calibration was performed with a certified reference material. For a temperature component, this may be checking that the temperature logs are accurate and that the setpoint is correct.

Stage 3: Apply the Moderate Correction

If the least invasive correction does not reduce the component, apply a moderate correction. This may include recalibrating the instrument, replacing a reagent lot, or providing refresher training to the operator. A moderate correction changes one element of the measurement process but does not change the method itself.

Stage 4: Apply the Major Correction

If the moderate correction does not reduce the component, apply a major correction. This may include changing the method, replacing the instrument, or redesigning the procedure. A major correction carries the highest risk and the highest cost, and it should be applied only after the earlier stages have failed.

Stage 5: Verify the Effect

After each stage, recalculate the uncertainty budget and compare the new total to the previous total. The verification must be done with the same method and the same data collection approach as the original budget. If the component has not decreased, the cause may be different from what you assumed, and the investigation must be repeated.

The Uncertainty Reduction Record System

A record system for uncertainty reduction is distinct from the uncertainty budget itself. The budget is a snapshot of the current state. The record system is a history of the actions taken, the results observed, and the decisions made. This history is what allows the laboratory to learn from past efforts and to avoid repeating actions that did not work.

The Reduction Log

The reduction log is a table that records each reduction action. The log must include the date, the component targeted, the action taken, the expected effect, the observed effect, and the decision to continue or stop. The log must be updated after each verification.

The log shows the history of each component. It also shows which actions were effective and which were not. The log is the basis for the next decision.

The Decision Record

The decision record documents the reasoning behind each action. It includes the effort impact matrix scores, the cost benefit analysis, and the rationale for the chosen action. The decision record is important for two reasons. First, it provides the evidence for the choice of action, which is useful for the quality manager and for the audit. Second, it prevents the laboratory from repeating an action that was already tried and failed.

The Verification Record

The verification record documents the data used to confirm the effect of the action. It includes the raw data, the calculated uncertainty components, and the comparison to the previous budget. The verification record must be complete enough that another person can reproduce the calculation and confirm the result.

The Review Schedule

The record system must be reviewed on a regular schedule. The review should include the uncertainty budget, the reduction log, and the decision record. The review should be done at least quarterly, and it should be done after any change to the method, the instrument, the reagent, or the operator. The review should be documented, and the findings should be communicated to the quality manager.

The Troubleshooting Decision Tree

A decision tree provides a structured path for the investigation of a high uncertainty component. The tree starts with the component that has the highest contribution and moves through a series of questions to identify the cause and the action.

Branch 1: Is the Component a Calibration Component?

If the component is related to the calibration standard, check the calibration certificate. Is the certificate current? Is the standard certified and traceable? If the certificate is expired, renew the calibration. If the standard is not certified, replace it with a certified reference material. If the certificate is current and the standard is certified, the cause may be the calibration procedure itself. Check the calibration procedure against the manufacturer instructions and the NIST guidance.

Branch 2: Is the Component an Environmental Component?

If the component is related to temperature, humidity, or other environmental conditions, check the environmental records. Are the conditions within the specified range? Are the monitoring devices calibrated? If the conditions are out of range, adjust the environmental control. If the monitoring devices are not calibrated, calibrate them. If the conditions are within range but the component is still high, the cause may be the sensitivity of the method to the environmental condition.

Branch 3: Is the Component an Operator Component?

If the component is related to the operator, observe the operator performing the procedure. Compare the technique to the written protocol. Check for common errors such as incorrect pipetting angle, incomplete mixing, or inconsistent timing. If the technique is incorrect, provide refresher training. If the technique is correct, the cause may be the variability between operators. Standardize the technique and consider using a single operator for the critical steps.

Branch 4: Is the Component a Reagent Component?

If the component is related to the reagent, check the reagent lot and the storage conditions. Is the reagent within the expiration date? Is the storage temperature correct? If the reagent is expired or improperly stored, replace it. If the reagent is new, compare its performance to the previous lot. If the reagent is the same lot and the storage is correct, the cause may be the reagent preparation. Check the preparation procedure and the water quality.

Branch 5: Is the Component a Method Component?

If the component is related to the method itself, check the method validation data. Is the uncertainty consistent with the expected performance? If the method has a known limitation, the uncertainty may be unavoidable. If the method is not performing as expected, the cause may be the instrument or the operator. The method component is the most difficult to reduce because it requires a change to the method, which carries the highest risk.

The Comparison of Reduction Strategies

The decision framework and the decision tree provide a structured approach, but the laboratory must also compare the reduction strategies to choose the best one. The comparison should include the following criteria.

The Expected Reduction

The expected reduction is the difference between the current component and the expected component after the action. The expected reduction must be estimated from the data, not from the assumption. For example, if the calibration component is 4.0 percent and the new calibration is expected to reduce it to 2.0 percent, the expected reduction is 2.0 percent. The expected reduction must be compared to the total uncertainty to determine the effect on the final result.

The Risk of the Action

The risk of the action is the probability that the action will not have the expected effect or that it will introduce a new source of uncertainty. A recalibration has a low risk if the calibration is performed by a qualified provider. A method change has a high risk because the method may not perform as expected. The risk must be included in the decision.

The Time to Implement

The time to implement is the time from the decision to the verification. A recalibration may take a few days. A method change may take several weeks. The time to implement must be considered in the context of the laboratory's workload and the urgency of the result.

The Cost

The cost includes the direct cost of the action and the indirect cost of the disruption to the routine testing. The cost must be compared to the benefit of the reduced uncertainty. The benefit is the improved reliability of the result and the reduced risk of a misdiagnosis or a regulatory failure.

The Escalation Decision

The decision framework includes the escalation criteria. The escalation is the point at which the laboratory cannot reduce the uncertainty with the available resources and must involve the quality manager or the laboratory director. The escalation is not a failure. It is a recognition that the uncertainty is a system-level issue that requires a system-level decision.

The escalation decision should be made when the following conditions are met. The uncertainty remains high after the corrective actions. The uncertainty is high for a method that is critical for the diagnostic decision. The uncertainty is high for a method that is used for the regulatory compliance. The uncertainty is high for a method that is used for the clinical trial.

The escalation should include the uncertainty budget, the reduction log, the decision record, and the verification record. The escalation should be documented and communicated to the quality manager. The quality manager should review the escalation and decide on the next steps. The quality manager may decide to change the method, the instrument, or the reagent. The quality manager may also decide to report the uncertainty to the clinician or the regulatory body.

The Role of the Quality Manager in the Decision Framework

The quality manager is the final decision maker in the uncertainty reduction process. The quality manager reviews the decision record, the reduction log, and the verification record. The quality manager decides whether the action is appropriate and whether the reduction is sufficient. The quality manager also decides when the uncertainty is acceptable and when it requires further action.

The quality manager must have the authority to change the method, the instrument, or the reagent. The quality manager must also have the authority to stop the testing if the uncertainty is too high for the intended use. The quality manager must be involved in the escalation decision and must be the final authority on the uncertainty.

The quality manager should also review the decision framework itself. The framework should be updated when the laboratory learns from the past actions. The framework should be reviewed at least once more and after any change to the method, the instrument, the reagent, or the operator. The review should be documented and the findings should be communicated to the laboratory staff.

The Practical Use of the Framework

The decision framework is a practical tool that the laboratory can use to reduce the uncertainty. The framework is not a substitute for the uncertainty budget or the Pareto analysis. The framework is a complement that directs the laboratory to the most effective actions.

The laboratory should use the framework in the following way. First, create the uncertainty budget and perform the Pareto analysis. Second, use the effort impact matrix to rank the reduction actions. Third, apply the reduction sequence to the dominant components. Fourth, record the actions and the verification in the reduction log. Fifth, review the log and the decision record on a regular schedule. Sixth, escalate the issue when the uncertainty cannot be reduced.

The framework is a cycle. The laboratory performs the analysis, applies the action, verifies the effect, and reviews the decision. The cycle is repeated until the uncertainty is acceptable. The cycle is documented, and the documentation is the basis for the next cycle.

The framework is also a learning tool. The laboratory learns from the past actions and the past decisions. The laboratory learns which actions are effective and which are not. The laboratory learns which components are reducible and which are not. The learning is recorded in the reduction log and the decision record, and the learning is used to improve the next cycle.

The framework is a practical way to reduce the uncertainty and to improve the reliability of the laboratory results. The framework is a systematic way to identify the dominant components, to prioritize the reduction actions, and to verify the effect. The framework is a tool that the laboratory can use to meet the regulatory requirements and to support the diagnostic decisions.

Frequently Asked Questions

What is the difference between measurement uncertainty and error?

Measurement uncertainty is the range of which the true value is expected to lie, while error is the difference between the measured value and the true value. Uncertainty is a parameter that describes the dispersion of the measurement, and it includes both random and systematic components. Error is a single value that can be corrected if it is identified.

How do I perform a Pareto analysis of my uncertainty components?

Calculate the contribution of each component to the combined uncertainty, then rank the components from largest to smallest. The top two or three components will typically account for most of the total uncertainty. Focus the corrective actions on these components.

What is the most common cause of high measurement uncertainty?

The most common cause is a lack of a systematic uncertainty budget. Without a budget, the laboratory cannot identify the dominant components and cannot target the corrective actions. The second most common cause is a calibration that is expired or misaligned.

How often should I review my uncertainty budget?

The uncertainty budget should be reviewed when the new instrument, reagent, or operator is introduced, and when the method is changed. The budget should also be reviewed when the control charts show an increase in the uncertainty. The review should be documented.

Can I reduce the uncertainty to zero?

No, the uncertainty cannot be reduced to zero. Every measurement has some uncertainty, and the uncertainty is a property of the measurement system. The goal is to reduce the uncertainty to a level that is acceptable for the intended use of the result.

What is the role of the calibration certificate in the uncertainty budget?

The calibration certificate provides the uncertainty of the calibration, which is a component of the uncertainty budget. The certificate also provides the traceability of the calibration to a national standard. The laboratory must use the certificate to estimate the uncertainty of the calibration.

How do I report the uncertainty in my results?

The uncertainty should be reported as the expanded uncertainty, which is the combined uncertainty multiplied by a coverage factor. The coverage factor is typically 2, which provides a confidence level of approximately 95 percent. The report should include the uncertainty value and the coverage factor.

What should I do if the uncertainty is high for a critical method?

If the uncertainty is high for a critical method, the laboratory should escalate the issue to the quality manager. The quality manager should review the uncertainty budget and the corrective actions. The quality manager may decide to change the method, the instrument, or the calibration.

Related Diagnostic Guides

• Measurement Uncertainty in Laboratory Medicine: A Guide to Calculation and Reporting
• Laboratory Inspection Checklists: Preparing for Regulatory Assessments
• Method Validation in Clinical Laboratories: A Practical Protocol
• Laboratory Quality Management Systems: Key Components and Implementation Strategies
• Laboratory Inspection Checklists: Ensuring Compliance with CAP and ISO Standards

References and Further Reading

• Molecular Biology and Laboratory Methods. National Library of Medicine.
• Biosafety in Microbiological and Biomedical Laboratories. CDC and NIH.
• NIH Guidelines for Recombinant or Synthetic Nucleic Acid Molecules. National Institutes of Health.
• Addgene Protocols. Addgene.
• MIQE Guidelines. Clinical Chemistry.
• NIST Laboratory Programs. National Institute of Standards and Technology.
• Laboratory Biosafety Manual. World Health Organization.
• NCBI Literature Resources. National Center for Biotechnology Information.
• Uncertainty of Measurement in Laboratory Medicine.. Journal of medical biochemistry, 2018.
• Measurement uncertainty as a universal concept: can it be universally applicable in routine laboratory practice?. Critical reviews in clinical laboratory sciences, 2021.
• Determination of measurement uncertainty in the serology laboratory for anti-HBs, anti-Rubella IgG, and anti-Toxoplasma IgG.. Diagnostic microbiology and infectious disease, 2026.

This article is educational and does not replace validated laboratory procedures, institutional biosafety review, manufacturer instructions, or professional interpretation.