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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 needs 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.

Let's go back to your target for a minute. This time, when you throw the darts, you are nowhere near the center, but at least you hit the same spot every time. In this case, we can say that while you were not accurate, you were precise. This is because precision, which is often called reproducibility or repeatability, is the agreement of repeated measures.

So, while accuracy refers to how close you are to your true value, precision refers to how often you get the same measurement under the same conditions. If, for example, you were to measure the volume of your chemical ten times, and each time you measured 75 ml (when the true value is still 60 ml), you would still not be very accurate. The good news, though, is that you were precise because you got the same value each time you took a measurement.

As with the examples we just saw, you can be accurate without being precise and precise without being accurate. But you can also be both accurate and precise (getting close to the 'true' value each time) or neither accurate nor precise (being far from the 'true' measurement and at a different value each time).

Types of Error

Ideally, you want to be both accurate and precise. But because neither humans nor the instruments we use to take measurements are perfect, any measurement is prone to error in some form. Human error includes any basic human mistake: spilling a substance, dropping equipment, forgetting to turn off the drying oven, etc. These errors can and will happen, no matter how avoidable they may seem in hindsight!

Any errors that are not directly attributed to human 'oopses' fall under two categories. The first is random error, which is error that occurs randomly in space and time. This type of error occurs periodically and in no specific pattern and, therefore, gives you imprecise results. Random errors are unavoidable and are just part of every dataset, but they are just as likely to occur in a positive or negative direction. This means that theoretically, after you take enough measurements, the random errors would essentially cancel themselves out, getting you closer to your 'true' value. Repetition works in your favor here!

The second type of non-human error is systematic error, which is error associated with the instruments. Perhaps your pipette is not dispensing the correct amount of fluid, or your scales are not calibrated correctly. In this case, you would have the same error each time because your instrumentation is off by the same amount for each measurement. In the end, all your measurements would be too high or too low, leading to precise, but inaccurate, data.

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