Identifying Sources of Unavoidable Experimental Error

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  • 0:01 Data Are Imperfect
  • 1:26 Experimental Error
  • 2:22 Accuracy and Precision
  • 3:15 Types of Experimental Error
  • 5:39 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.

Good data are essential for any experiment. But no matter how hard you try, error in your data is unavoidable. In this lesson, you'll learn about the types of unavoidable experimental error to be on the lookout for and how to reduce their occurrence in your data.

Data Are Imperfect

People generally associate data with scientific investigations, but in reality, we all use different kinds of data every day. Data can be as complex as quantum physics calculations, or as simple as the air pressure in your tires. And while data come in all forms, shapes, sizes, and values, what's universal is that the data we use need to be the best data possible.

But what are 'good' data? Well, imagine, for example, that your local weather person is reporting on a hurricane headed your way. You better hope that the data they have are top notch, because the information they present to you will influence whether or not you choose to evacuate and how quickly you plan to leave.

Unfortunately, no matter how thorough and careful you are, your data are technically false. This is because they're simply representations of reality that we use to help describe our world. This falseness of data is called error, which means variation when referring to data. It's not error in the sense of 'whoops, I spilled that jar of chemicals,' or 'I wrote down the wrong measurement.' Error, or variation in data, simply refers to whatever causes our data to be imperfect, not human mistakes.

And even though no data are perfect, some are still better than others. Remember, your data are representations, and you want them to be the best representations they can be. In order to achieve this, you need to be familiar with these unavoidable sources of error and how they occur so that you can minimize their influence and get the best data possible.

Experimental Error

There's lots of things that you might hear people refer to as 'error' in an experiment, but in reality, there are only a few things that fall into that category. First, remember how I said that human error isn't really experimental error? Human mistakes are avoidable and, therefore, not considered part of the natural variation that occurs with data. Of course, you do need to be as careful as possible because your mistakes will certainly affect the outcome of your experiment. But you can control this for the most part.

So, what is experimental error, then? Experimental error is the difference between a measurement and its accepted value. For example, the weight of an object is rarely an exact measurement. You may get on the scale and see a value of 160 pounds, but in reality, your weight might be 160.11111 pounds, which would be difficult to measure on a common bathroom scale. The difference between these two is what we would call the error.

Accuracy and Precision

Before we go any further, we need to review a couple of terms, which will help us better understand experimental error. The first is accuracy, which refers to how close a measurement is to the 'true' value. For example, if you step on that scale and it reads 160 lbs, that's pretty darn close to the 'real' value of your weight (160.11111 lbs), and we would consider this an accurate measurement.

The second term is precision, which is the agreement of repeated measures. If you get on that scale and your weight reads 160 lbs each time, we would consider the scale to be very precise because the measurements are the same each time. But if you get on the scale and the first time it reads 160 lbs, the second time it reads 155 lbs, and the third time it reads 163 lbs, it's not very precise at all because each time you measure your weight you get a different value.

Types of Experimental Error

Okay. Now that we've gotten that out of the way, let's get back to experimental error. There's two types of experimental error to be familiar with. The first is systematic error, also called 'procedural error.' Systematic errors tend to skew your data in one direction or the other. This type of error gives you precision because your error will be the same each time (the 'systematic' part of the error), but generally leads to inaccurate data because they are off from the 'true' value.

How does systematic error occur? It usually comes from a problem with the measuring equipment itself. A machine may not have been calibrated correctly or perhaps there is simply something wrong with the equipment internally. So, if your scale reads five pounds too heavy each time, then each time you measure your weight, it will be five pounds over your 'real' weight. Precise, but not accurate (and not how I want my scale to read!). Sometimes we can prevent systematic errors, but sometimes we cannot.

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