Reaching & Applying Scientific Conclusions

Instructor: Amanda Robb

Amanda holds a Masters in Science from Tufts Medical School in Cellular and Molecular Physiology. She has taught high school Biology and Physics for 8 years.

In this lesson we'll look at the basics of inquiry investigations in science. After learning how to analyze data, we'll determine the steps for making conclusions and comparing alternate conclusions in science.

Scientific Experiments and Conclusions

You're in biology class conducting an investigation on osmosis. You design an experiment to test which solutions cause water to go into a cell versus out of a cell. Your group tries the experiment three times and gets the same result.

Another group only does the experiment once, but gets a different result. To make matters more complicated, another group confirms your result, but gives a different explanation for the data. Who's data is more correct? How will you reconcile all these different results for your lab report?

The process of analyzing data and making meaning of it is called drawing conclusions in science. Evaluating scientific data is a key feature of being a scientist. Today, we're going to learn what methods are most reliable for gathering data, how to analyze results and finally draw conclusions, including comparing multiple explanations for the same data.

Gathering Data

The first step in any experiment is to gather data. Although this may seem simple, the process of gathering data can make or break a conclusion. Recall the beginning of this lesson where your group conducted the experiment three times and another group conducted an experiment only once. Which set of data is more reliable?

The more times an experiment is repeated and produces the same outcome, the more reliable the data is. A result that only occurs once is much more likely to be due to chance than any scientific principle. More trials equals more accurate data, and more accurate data will give you a more meaningful conclusion.

Running experiments multiple times increases data reliability

Analyzing Results

Now that you have your reliable data, it's time to analyze, or look for patterns in that data. At this point, it's helpful to make a chart or graph to organize your data. Ask yourself what you notice? Are there any differences between samples or trends?

Let's say you're studying photosynthesis. To do this you put spinach leaves in water, with or without carbon dioxide. You hypothesize that only the leaves with carbon dioxide will do photosynthesis.

When you preform your experiment, you see a trend that the more carbon dioxide the leaves were given, the more oxygen bubbles were produced. This is a trend and an important part of your analysis. During the analysis phase, you're looking for facts, trends, or patterns in your data, not necessarily making conclusions yet.

Analysis includes noticing trends in graphs such as the decrease in migraines in this graph

Drawing Conclusions

Now that you've noticed some patterns, it's time to make conclusions and figure out what that analysis means in context of science. In your photosynthesis experiment, you saw oxygen bubbles produced by the spinach in carbon dioxide rich water. What does that mean?

Using your background research, you know that photosynthesis makes oxygen. So, if the spinach makes oxygen bubbles, wouldn't that mean they are doing photosynthesis? And if the spinach with no carbon dioxide makes no bubbles, you can come to the conclusion that photosynthesis requires carbon dioxide.

How solid is this conclusion? Well, how can you be sure the bubbles are oxygen and not something else? Is that a reasonable assumption? Could you test that theory?

When coming to conclusions it's important to try to pick apart your own explanation. Think critically about other explanations for the same data, as if you are trying to prove yourself wrong. Science isn't about being wrong or right in your prediction, but rather coming to a solid conclusion based in evidence.

There is no bias in science, or having a preference for one answer. Scientists look strictly at the facts with no emotional attachment to their hypothesis.

Scientists must examine data without bias

Your job as a scientist is to defend your conclusion using only evidence from your experiment. If you don't have enough evidence to hold up your conclusion it's back to the drawing board. It doesn't make you a bad scientist. In fact, scientists learn just as much or more from incorrect hypotheses as they do from correct ones.

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