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Evaluating Data from Scientific Investigation

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Instructor: David Wood

David has taught Honors Physics, AP Physics, IB Physics and general science courses. He has a Masters in Education, and a Bachelors in Physics.

Data collected from scientific experiments can be analyzed in its accuracy (correctness), reproducibility, and precision of measurements. Learn how these three factors are used to analyze scientific data. Updated: 11/03/2021

What Is a Science Experiment?

A science experiment is a way of figuring out the structure and behavior of the world using a systematic method. In any experiment, you change one variable, called the independent variable, and see how it affects another variable, called the dependent variable. Everything else must be kept the same; otherwise, it won't be a fair test.

For example, perhaps you're investigating how the amount of watering affects the height that grass grows. The amount of water used would be your independent variable, and the height of the grass would be your dependent variable. You would try to keep everything else the same; you would make sure the grass gets exactly the same amount of sunlight, is growing in the same kind of soil, and is in the same location.

But not all data in science experiments is of the same quality. Some people are better at measuring things than others, and sometimes there can be issues that cause all of your data to be off by a certain amount. So today, we're going to talk about the accuracy and reproducibility of data.

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  • 0:02 What Is a Science Experiment?
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  • 1:40 Reproducability or Precision
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Accuracy of Data

The accuracy of data tells you how close to being correct your data is. For example, perhaps you measured your grass using a ruler, but instead of measuring from the zero line, you measured from the edge of the ruler. That would make all your grass-length measurements wrong by a certain amount. Maybe the grass was always one centimeter shorter than you thought. That causes your data to be less accurate.

Or maybe you didn't notice that the ground was already wet when you started the experiment because it had rained that morning! So, all your grass got an extra inch of rainfall that you didn't measure. This would also make your data less accurate.

Reproducibility or Precision of Data

Accuracy and precision, otherwise known as reproducibility, are not the same thing. The reproducibility, or precision, of data tells you how close together your measurements are. Let's say that when you measured the grass, you measured it ten times to make sure your number was right. Perhaps you measured ten different blades of grass and took an average of your numbers. If your measurements came out as follows: 7.1 centimeters, 7.2, 7.0, 7.1, 7.2, 7.1, 7.1, 7.0, 7.1, and 7.1, that would be more accurate than if your measurements came out like this: 7.1, 7.4, 7.5, 7.4, 6.7, 6.8, 6.8, 6.8, 6.6, and 7.6; even though both times you got an average length of about 7.1 centimeters. You could say that both pieces of data were just as accurate, but the first set of data was more precise, more reproducible.

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