When an accredited environmental laboratory reports data, many people’s first instinct is to trust it completely. The laboratory follows requirements set by regulators and performs analytical testing according to relevant methods, federal and state regulations and its own standard operating procedures (SOPs). That foundation matters, but it does not answer every question.
For more than 30 years, our team has helped clients test that assumption. Data validation remains one of the most common quality assurance (QA) services our clients request. It is a systematic review of analytical data against established criteria to assess whether results meet method requirements, SOPs and other standards, and whether they are suitable for use. The process also identifies potential usability issues before decisions are made.
Validation, however, is not the only way to assess data quality. On some projects, reviewing historical data alongside data validation has uncovered both sample-specific and systematic laboratory issues. That matters because laboratory data can be compliant and appear high-quality yet still fail to represent the location where the sample was collected.
Comparing current results with historical trends helps teams identify possible sources of error, confirm data integrity and act on stronger evidence. That can improve outcomes and support better decision-making.
When historical data review adds value
Historical data review compares reported data with previous analytical results from a specific sampling location. To avoid bias, it should be conducted separately from the data validation process. The objective is different. Data validation tests compliance and usability. Historical review helps identify issues such as occasional field or laboratory contamination and possible sample switches.
This approach is most useful when a project has the right foundation:
- A robust dataset, ideally at least four to five previous results
- Routinely monitored water samples or other matrices sampled often enough to show a pattern
- Consistent sample locations, such as an installed monitoring well, surface water station or soil sample collected from the same GPS coordinates
- Once a project is established as a good fit, teams can choose the review method that best matches the data and the question at hand:
- Tabular review, which compares current results directly with previous results
- Historical time series, which shows trends graphically over time
- Statistical limits, which set upper and lower thresholds but may miss anomalies depending on how the limits are defined
Whatever the method, the review may produce a list of outliers when a new dataset is evaluated. That is where the real investigation begins.
How the investigation moves forward
When historical review surfaces an issue, the next step is to weigh multiple lines of evidence. The central question is simple: do the analytical data support an error?
A thorough review of the laboratory data package helps answer that question. Additional analytical runs or further analyses may support or refute the result in question. It is also important to look at broader patterns. Seasonal trends can matter, and the review timeline may need to extend further back if enough data are available.
Field data can add essential context. Measurements such as pH, oxidation-reduction potential (ORP) and specific conductance often indicate whether conditions at a location have changed. Sampling notes can also help, especially where weather may have affected results. For groundwater samples, flooding or drought conditions can influence analytical results.
If the evidence does not explain the deviation from historical results, the laboratory should review and confirm the reported data, with support from field teams when needed.
At that point, the investigation returns to the source. Field or laboratory personnel review their documentation, reanalyze the sample if necessary and report revised results when the evidence supports a correction. When re-reporting is required, the process needs thorough documentation so teams can examine each step and identify the root cause of the issue that affected the original result.
The goal is always data integrity and accuracy. In some cases, the strongest way to protect both is to make better use of the data already in hand.
Case study: sample contamination
In one case, chromium concentrations in a field duplicate, a second sample collected to check consistency, were significantly higher than previous results from that location, and the historical record offered no explanation for the spike. Because the issue appeared in the field duplicate, the parent sample result provided strong evidence of a potential laboratory problem.
The laboratory was asked to confirm the reported result and could not. It chose to reanalyze the sample and report the reanalysis results. The broader review also showed several incidents of isolated contamination, which suggested the issue was becoming systemic. A corrective action followed, and the laboratory improved its cleaning procedures and updated processes to help reduce future contamination.
Case study: sample switch in the laboratory
In another case, historical data review indicated that a sample switch had occurred during metals analysis, swapping the results for Well A and Well B. The same issue did not appear in the anions or general chemistry results for those samples, which strengthened the case that the problem was limited to the metals laboratory.
The laboratory reviewed the data, completed a reanalysis and confirmed that a sample switch had occurred. Revised results were then reported to restore confidence in the data for each well location. Because similar sample switches had occurred before in the same department, the incident also required formal corrective action.
Without historical data review, the error would not have been obvious. The metals data met method requirements, and laboratory quality control samples did not indicate a problem.
Better decisions start with better context
Data validation remains essential. It provides a disciplined review of whether analytical results meet established requirements. Historical data review adds something equally important: context. It helps teams judge whether compliant data also make sense for the location, the sampling history and the field conditions.
Programs that pair validation with historical review are better positioned to spot contamination, trace sample switches and correct process issues before they distort decisions. Sometimes the best way to protect current results is to look carefully at the results that came before them.