Data Interpretation
Our updated manual is available in PDF format
Manual • Onota Example
Brief Overview
Interpreting your data is a process that involves answering a series of questions about it. We suggest the following steps:
1) Review and interpret the data "in-house" to develop preliminary
findings, conclusions,
and recommendations.
2) Review the data and your interpretation of it with an advisory group or
technical committee. This group should involve local, regional, and state
resource people who are familiar with monitoring and with your water body.
They can verify, add to, or correct your interpretation of the results.
3) Review the data and your interpretation of it with the people who will
use your data -- for example, the public, water body users, and government
officials.
Ultimately, your interpretation of the data relates back to the questions your monitoring program is trying to answer. For example, does the water body meet state water quality standards? Following are examples of questions you might answer at each step in order to develop findings and conclusions that relate to your study questions.
Findings
Findings are observations about your data. To come up with findings, try to answer a number of questions about your data, such as:
Analyze your Quality Assurance/Quality Control Results
Quality assurance/quality control measures are undertaken to determine how
reliable your data are. Common measures include split, duplicate, spiked replicate,
known, unknown, and blank samples. How do your Quality Assurance results compare
with expected results? Did they meet your data quality goals? If you haven't
met these goals, then your data may not be useful to answer your question.
- Split, duplicate and replicate samples: For each of these quality checks, you have two results that need to be compared. How close were they? Did they meet your expectations?
- Spiked, known, and unknown samples: For this check, you compare actual with expected results. How close were they? Did they meet your expectations?
- Blanks: For this quality check, the result should be "0"
Compare you results with known standards or guidelines
State water quality standards contain criteria which are numbers that define
acceptable levels of common water quality indicators. If criteria are not
available for an indicator you've measured, consult a water quality advisor
as to appropriate numerical guidelines against which you can compare your
results.
- Are there sites that consistently exceeded (violated) water quality standards or guidelines? By how much?
- Are there sampling dates where most or all sites consistently exceeded (violated) water quality standards or guidelines?
Compare you results within your data set
These questions help you use your own data to focus on upstream to downstream,
or inlet/lake center comparisons and comparisons over time.
- Which sites had the highest or lowest readings?
- Which dates had the highest or lowest readings?
- Are there numbers which seem to be much higher or much lower than typical results ("outliers")?
- Do you have confidence that these numbers are reliable? Verify that these numbers were transcribed or entered correctly.
- Do your results show a consistent pattern of change upstream to downstream, or, say, from the epilimnion to the hypolimnion?
- Do levels increase or decrease in a consistent manner?
- What were the flow and rainfall like on your sampling dates? Was there heavy rain? Was the flow/ water level rising or falling? If you are monitoring the impact of a pollution source, for example, are your results different above and below (near/away) the impact?
- Do changes in one indicator coincide with changes in another? For example, there is frequently an inverse relationship between water temperature and dissolved oxygen, since warm water can hold less oxygen than cold water.
- How do your results compare among tributaries/sites?
- How do your results compare with reference conditions that you establish? Reference conditions are a description of the best attainable conditions for your water body. They may be conditions that describe the least developed part of your watershed. They may be conditions that describe a similar water body in another watershed. In either case, you compare your results with these conditions to determine whether conditions in your water body are meeting the expectations of experts.
Compare your results with other data sets
Monitoring data from other sources might help you put your results in
perspective. Be aware, however, that the data must have been collected using
comparable methods, or the comparison is not valid.
- How do your findings compare with other data sets (e.g. state reports)?
- How do your results compare with those of other water bodies (similar or not)?
- How do your results compare with reference conditions that others establish? For example, water quality standards are a type of reference condition established by your state legislature. However, the standards may not address the indicators you are monitoring. Your state agency advisor can tell you if reference conditions have been established for your watershed or for a similar watershed.
Conclusions
Conclusions are your explanation of why the data look the way they do. Your conclusions should relate back to the questions you asked at the beginning of your monitoring program - your study questions. The following are examples of questions that might enable you to answer your study questions:
- Does weather appear to influence your results? For example: Do high levels coincide with heavy rainfall (consider the intensity and duration)?
- Do high levels coincide with rising flow (consider flow management impacts) or with low lake levels?
- Do flaws in your field and/or laboratory techniques explain your results?
- Do you have confidence in your quality control lab? Don't automatically assume that if your results differ from your quality control lab that the problem is with your own lab.
- If you are monitoring the impact of a pollution source, does the presence of this source explain your results? For example, can you attribute increased bacteria/ nutrient levels to the source you're monitoring?
- If you are monitoring the impact of a pollution source, are there other upstream/ nearby impacts which might influence/confuse your results?
- Might natural changes explain your results? For example, ponds and wetlands can influence river bacteria levels and stream critters just downstream. Or, the presence of weeds or algae can raise pH during summer days.
- Did the time of day you sampled affect your results? For example, dissolved oxygen is typically lowest in the morning and highest in the late afternoon.
- For episodic discharges, did your sampling coincide with the discharge? For example, did you catch the storm-related polluted runoff you were trying to analyze?
- Was the flow rising when you collected your samples, indicating that runoff was increasing river flow? Some point source discharges are not constant.
- Did you catch the discharge?
- Do changes in one of your indicators appear to explain changes in another? For example, could low dissolved oxygen be explained by high temperatures?
- Do your visual observations explain any of your results? For multiple years of data, what are some overall trends? For example, did the benthic macroinvertebrate community improve or deteriorate over time?
- Did this coincide with improved pollution control or new pollution sources?
- What other information might you need in order to explain your results (such as a shoreline survey, habitat assessment, or additional indicators)?
- Did changes in land use or population density in the watershed explain your results? For example, has population density changed? Were vacation homes converted to year-round residences?
A final word: remember that your data may be inconclusive, especially after only a year of monitoring.
Recommendations
Based on your findings and conclusions, what do you recommend?
- For action to address any problems you identify
- For further information you might need in order to draw conclusions and make recommendations for action. For example, you may need to sample more frequently, or at different times (wet weather sampling, early morning sampling...) You may want to add some other indicators.
Onota Example
See this pdf document for a data analysis summary example. Lake Onota assembled a panel of experts to review and analyze water quality data in order to evaluate environmental monitoring practices and the health of Lake Onota. Onota is also used as a case study in our updated data interpretation manual.
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