Step 4: Analyze & Communicate Your Data

Was my hypothesis conclusively right or wrong? What next? 

At this point, you should be able to tell if your data not only proves or disproves your hypothesis, but whether it does so conclusively. Conclusively means you can say without much doubt that your hypothesis was right or wrong and the reasons why. If you cannot do this, then there are additional steps (like replicating the data, collecting seasonal data or using another testing method) you can take to get to a sounder conclusion.

For example, let’s say that your data shows a dissolved oxygen range, a temperature range and a turbidity reading that all fall within the values needed to support aquatic life. However, your pH range is much higher than the normal range for healthy Montana streams. Is this enough evidence to conclude that your stream cannot support aquatic life?

Some additional steps would be to collect additional pH data at different times in the same location to see if the pH is always abnormally high, or to conduct macroinvertebrate sampling – a different biological monitoring technique we will learn in a future module – to give you additional data relevant to aquatic health of the site. Another step would be, either before or after collecting additional data, to contact some of the sources you used in your data mining step and compare your results to see if they might be able to help you find an explanation.

Even if your hypothesis was proven conclusively, there are always additional steps you can take. If your data says that your stream should support aquatic life: What steps can we take to confirm it and to preserve and protect its quality? If your stream does not support aquatic life: Why not? How can we improve the quality of our stream so it can support life?

The beauty of science is that it is a never-ending process, and it becomes more relevant through a place-based inquiry approach, such as water quality monitoring. In the end it can become a lifelong passion that benefits the whole community and ecosystem.

In this short video, Instructor Matt Vincent discusses how to interpret you water monitoring data and explores options for further investigation using the example of German Gulch Creek west of Butte.

Right-click or ctrl-click this link to download.


As an extension activity, we HIGHLY recommend entering your data online in the World Water Monitoring Day database. If you have good, sound data, it is worth sharing: with your school, your community, even the world! (And it’s easy. It only takes a few minutes once you’re registered.)

This step gives your students’ data “world recognition”, so to speak, making it a lot more meaningful and, most importantly, allows you to compare your data to hundreds of other locations around the globe. A lesson in science can become a lesson in world geography! After all, water is of worldly importance and this last step cements the point that everyone needs to care about water. Oh yeah, we forgot to mention: if you do enter your World Water Monitoring Day data into their database, you could also win prizes!

Check out how you can participate in the World Water Monitoring Challenge by clicking here.


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The MSP project is funded by an ESEA, Title II Part B Mathematics and Science Partnership Grant through the Montana Office of Public Instruction. MSP was developed by the Clark Fork Watershed Education Program and faculty from Montana Tech of The University of Montana and Montana State University, with support from other Montana University System Faculty.