In laboratory testing, the terms accuracy and precision are often used interchangeably. However, they represent two distinct concepts that are both equally crucial for reliable results. This blog talks about how accuracy and precision play important roles in ensuring the integrity of your lab results - and why they both matter more than you might think.
What is Accuracy?
Accuracy refers to the closeness of a measured value to the true value of the analyte being tested. In other words, accuracy assesses how correct a measurement it. For instance, in the case of a blood glucose test, if a patient’s actual blood glucose level is at 100mg/dL, an accurate test would produce a result that very close to that value.
Imagine accuracy as a target where the bullseye represents the true value. If a measurement lands right on the bullseye, a test has matched the correct and true value!
Since accuracy is essential to ensuring patient results are reliable, it is critical to explore the various errors that can impact a test’s correctness. Let’s discuss more about the errors linked to accuracy:
Systematic Error
Systematic Error is the difference observed from the measured value and the actual value. This is also commonly known in experiment application as Bias. Systematic errors are predictable and consistent which occur repeatedly and skew results in a specific direction. Common sources of these errors include:
- Instrumental Error: Instrument with incorrect calibration.
- Method Error: Implementing a method that is unable to determine the analyte of interest.
- Personnel Error: A laboratory scientist habitually reads a scale incorrectly.
All these examples may cause test results to either be too high or too low. To mitigate systematic errors, the issues must be identified and corrected before conducting a laboratory test. Calibration should be performed regularly, and techniques checked for proficiency.
Accuracy in Method Verification
During Method Verification, testing is performed to quantify any systematic error which determines whether a method is reliable. Experiments such as Method Comparison and Trueness are specifically designed to assess a method’s ability to produce accurate results.
Both experiments follow the same principle where pooled samples are tested under identical conditions - one using the new method and one using an already established method to estimate any potential bias.
Below is a brief overview of how these experiments are conducted:
- Total Allowable Errors (TEas) are established to determine the maximum allowable systematic error or bias
- The results from the established method are taken as the true value.
- The results from the new method are then compared to the true values for evaluation.
- For a method to be considered accurate, the experimental systematic error (or bias) must not exceed the established Total Error Allowable (TEa).
Interested in learning more about Method Comparison and Trueness? Check out Cualia’s comprehensive eBook guide on Method Verification Experiments for more details!
What is Precision?
Precision refers to the reproducibility of measurements, indicating the consistency of results across repeated tests. A precise method will consistently produce similar results across multiple tests, even if those results are not necessarily accurate.
Imagine precision as a target - regardless of whether you are hitting the bullseye. If a test is consistently hitting the same spot repeatedly, then these results are precise each time!
In addition to accuracy, a method should also demonstrate precision to be considered reliable. Let’s take a closer look at errors associated with precision:
Random Errors
The difference in values from repeated testing of the same sample is called Random Error or imprecision. Random errors occur unpredictably throughout an experiment, sometimes without identifiable causes. These can be caused by a number of factors including:
- Environmental Fluctuations: Variations in room temperature or air pressure.
- Observation Variation: Differences in how scientists reads a measurement each time.
- Instrument Fluctuation: Minor vibrations in an instrument.
These sources of errors introduce random variations, leading to unpredictable differences in test results. Although unavoidable in a laboratory setting, understanding their sources allows scientists to design experiments that minimize their effect to maximize precision and reliability.
Precision in Method Verification
During Method Verification, testing is also performed to estimate random errors present. A Replication Experiment is performed where statistical metrics, such as standard deviation (SD) and CV%, are used to quantify the variation in results.
Here is a brief overview of how a Replication Experiment is conducted:
- A laboratory establishes the maximum coefficient of variation (CV%) they are willing to tolerate.
- Pooled samples ideally covering the full working range of the analyte (low, medium, high) are used.
- These samples are tested under the same working conditions over several days to assess a method’s repeatability.
- If the experimental CV% falls below the predefined maximum CV%, then a method is considered as precise.
Interested in learning more about Replication Experiments? Check out Cualia’s eBook guide on Method Verification (MV) Experiments for an in-depth guide!
Accuracy and Precision: The Perfect Scenario
In laboratory procedures, it is essential to ensure both accuracy and precision in the results is crucial. A result can be accurate but not precise, or precise but not accurate. However, reliable test results require both accuracy and precision to ensure consistency and correctness.
Resources
Westgard, James. (2020). Basic Method Validation, 4th Edition. Wisconsin, Westgard QC, Inc.
Raeburn, A. (2022, May). What's the difference between accuracy and precision?. Asana. Retrieved from: https://asana.com/resources/accuracy-vs-precision
Flowers, P., Theopold, K., Langley, R., Robinson, W., (2019, February). Chemistry 2e. Texas, Openstax. Book URL: https://openstax.org/books/chemistry-2e/pages/1-introduction
What is the Difference between Accuracy and Precision Measurements?. (2023). Retrieved from: https://www.precisa.co.uk/difference-between-accuracy-and-precision-measurements/
Bhandari, P. (2021, May). Random vs. Systematic Error | Definition & Examples. Scribbr. Retrieved from: https://www.scribbr.com/methodology/random-vs-systematic-error/#:~:text=Random error introduces variability between,value in a specific direction.
Helmenstine, A.M., (2020, July). Random Error vs. Systematic Error. ThoughtCo. Retrieved from: https://www.thoughtco.com/random-vs-systematic-error-4175358