Explaining Results With Evidence

By the end of this lesson, you will be able to build an explanation and suggest a solution that is backed up by data and matches what scientists already know.

Imagine your class notices that the school parking lot near the football field floods every time a big Texas thunderstorm rolls through. You collect data, which is the measurements and observations you gather, such as rainfall amounts, how deep the water gets, and how long it takes to drain. You also build a model, a simplified representation of a real system, by making a sloped tray of soil and pavement and pouring water over it to watch where the water goes. Your tray shows water running off the smooth pavement quickly and pooling at the lowest corner.

Now you can write an explanation: the parking lot floods because pavement does not let water soak in, so rain runs downhill and collects in the low spot. Notice that this explanation does more than describe what happened. It uses your rainfall measurements as evidence, it matches your model, and it agrees with a scientific idea you already learned, that water moves downhill and soaks into permeable ground more easily than into solid surfaces.

Next comes the proposed solution. You might suggest adding a rain garden at the low corner or replacing part of the pavement with gravel. A good solution is not just a guess. You should be able to point to the data and the model and say why your idea should work, and you should be able to test it, even in a small way, by changing your tray and pouring water again.

Strong scientific explanations always connect three things: the data you collected, the model or pattern you observed, and the science ideas that are already well supported.

Try it: If your tray with gravel drained water faster than the tray with pavement, what would that evidence tell you about your proposed solution?