Evaluating Data: Precision, Accuracy & Error

Evaluating Data: Precision, Accuracy & Error
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  • 0:03 Why Evaluate Data?
  • 1:12 Accuracy & Precision
  • 3:30 Types of Errors
  • 5:50 Lesson Summary
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Lesson Transcript
Instructor: Sarah Friedl

Sarah has two Master's, one in Zoology and one in GIS, a Bachelor's in Biology, and has taught college level Physical Science and Biology.

The data you present as a scientist need to be as accurate, precise and error-free as possible. In this lesson, we'll discuss what each of these terms means, as well as how error is introduced into measurements and other data collection.

Why Evaluate Data?

When you're reading someone else's scientific work (or even when you're reviewing your own), you should always carefully examine the data presented. Scientific data can influence policymaking and other important decisions, so having good data is key. Trustworthy data lead to better informed decisions, greater scientific credibility and can even point out where your study may need to be tweaked or redesigned.

One way that we can evaluate our data is through replication. Performing certain steps or even the entire experiment over and over again gives us more information than what we get in just one pass. But even if you very carefully and thoroughly perform the exact same steps each time, you may find that you get different results. Does this mean you have bad data? Not necessarily. But it might help you identify different types of errors associated with your data.

You've probably heard the terms accuracy, precision and error before, but what exactly do those terms mean? Let's go through each of these in order to give you a better idea of how they relate to scientific data - yours or anyone else's.

Accuracy & Precision

Spot on! You hit the nail on the head! Bullseye! You've heard these statements if you correctly guessed the right answer to a question. Just like in that quiz, accuracy of scientific data refers to how close a measurement is to the 'true' value.

Let's take that bullseye for example. If you had a target that you were throwing darts at, and you hit the very center of the target each time, you would be very accurate because that is the 'true' value you are trying to achieve. But if you hit far outside the center of the target each time, you would be inaccurate because you are not near the 'true' value. The farther you are from the center, the less accurate your throws.

Accuracy is very important in scientific data collection. For example, you may wish to measure the volume of a certain chemical in your experiment. If the actual volume was 60 ml but you measured 75 ml, you would not have a very accurate value because it is not close to the 'true' value of 60 ml. But if you measured 59 ml you might consider this an accurate value since it is so close to the 'true' volume.

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