Video: Experimental Measurements

In this lesson, we will learn how to recognize types of experimental variables and to understand how variables are used in performing experiments.

13:36

Video Transcript

In this video, we’re talking about experimental measurements. Whenever a scientific experiment is conducted, it always involves coordinating a series of factors called variables and constants. In order to make measurements to test a prediction. In this lesson, we’ll learn about variables and constants. And we’ll see how they impact experimental measurements.

As we get started, say that we have this wooden ramp. And at the top of the ramp, there are these four rolling objects. The first object is a sphere. The second is a small cylinder. Then we have a thin ring and then finally a thick ring. And say that we make a prediction that if we release all four of these objects at the same time from the top of this ramp. Then we predict that the small cylinder will reach the bottom of the ramp first.

Now any time we make a prediction about a repeatable physical event, the best way to test that prediction is to perform a scientific experiment. Indeed, the general purpose of experiments is to test predictions. Whenever we design an experiment, it’s important to be aware of two types of factors that we mentioned earlier, variables and constants.

When it comes to experimental variables, there are a number of different types. For example, there are variables that we change or vary on purpose. In our rolling object experiment, that variable is the shape of the object that’s rolling down the ramp. The name we give to a variable that we change on purpose in a deliberate way is we call it an independent variable. This is the critical factor that we change in an experiment in order to test our prediction.

And in a well-designed experiment, a change in an independent variable impacts what’s called a dependent variable. And this is just what we might think. It’s a variable that depends on or relies on another one, the independent variable.

In the case of our ramp experiment, while the independent variable is the shape of the object rolling down the ramp. The dependent variable would be the time it takes for an object to reach the bottom. That’s because that time depends on the shape of the object rolling down the ramp.

In general, a good way to understand an experiment and what it’s about is to identify the independent and dependent variables involved. Outside of these types though, there are still other kinds of variables. Consider our particular experiment.

Let’s think of the materials that our four rolling objects are made of. We can see that if the objects are made of different materials, that could affect our experimental results. For example, what if our sphere is made of concrete, while our small cylinder is made of a light wood, while our thin ring is made of plastic and our thick ring is made of glass?

We can see then that the material our rolling objects are made of is potentially a variable. But it’s possible for that to be what’s called a controlled variable by ensuring through our experimental design that all the materials are the same. Since our experiment is designed to test which of these four shapes is fastest to reach the bottom of the ramp. We’d like to control the material these objects are made of so that it’s the same so that it’s not a factor that these speeds might vary by. So a controlled variable is something in an experiment that could change. But we take care to ensure that it doesn’t.

In contrast to this, there’s a type of variable called an uncontrolled variable. Let’s say that, in our ramp experiment, we didn’t have the materials or the equipment necessary to ensure that the surface of our ramp was evenly smooth. In that case, the ramp we use in our experiment might have some rough patches in it or some divots or scratches. If there’s nothing we can do to correct this situation, if there’s no way we can evenly smooth out our ramp, then indeed the ramp surface is an uncontrolled variable in this experiment.

And we can see how this uncontrolled variable has the potential to disrupt our experiment. Because now if we let all of these objects roll down the ramp, and we record which one reaches the bottom first. We won’t know if that’s due to its shape or whether it had the smoothest path on the ramp to roll on, or some combination of the two. Our uncontrolled variable upsets the integrity of our experiment.

So it’s very important when designing an experiment to be aware of all the uncontrolled variables involved. It may be possible to mitigate their impact or even redesign the experiment to eliminate some of them. We see then these four types of variables: independent, dependent, controlled, and uncontrolled.

Now let’s move on to consider the constants involved in experiments. Generally, there are two types. One type that we simply call constants are factors in an experiment that could change. But we take care so that they don’t. As we saw in our ramp experiment, one factor that could be different from rolling object to rolling object is the material that the object is made of. But if we deliberately choose to make them all out of the same material, then that material type is a constant.

Recall that we said earlier that if there’s a factor in an experiment that could change but we take care so that it doesn’t, that’s called a controlled variable. Therefore, a constant and a controlled variable are essentially the same thing. They both represent something in an experiment that might change but does not.

This type of constant is different from what’s called a universal constant. A universal constant is something not only that does not change but that cannot change. If we think on a very small scale, we can see an example of this. We know that electrons, negatively charged particles, have a certain amount of electric charge. This charge is fixed per electron. Every electron has the same exact amount of charge. And this is a constant. And in fact, the charge on an electron is a universal constant. It’s not something that can change. It will always be the same no matter what.

So we see then how a universal constant is different from a regular or simple constant. A constant, which we can also call a controlled variable, is something that can change but does not. While a universal constant is not able to change even if we wanted it to.

Now that we’ve looked at variables and constants, let’s go back to our experiment and recall what the independent and dependent variables here were. The independent variable, the factor we were changing on purpose, is the shape of the objects that are rolling down this incline. The dependent variable was the time it takes for them to reach the bottom of the ramp.

Now let’s think about that time. We could say that that amount of time for a particular shape is a quantity. That is, it’s an amount of something, in this case an amount of time. As part of the experiment, we make a measurement of that quantity, probably using a stopwatch. That measurement gives us what’s called a value. So in other words, there is some quantity of time that each object took to reach the bottom of the ramp. And when we measured that quantity, we returned a value. And it’s these values, one for each shape, that we want to compare in order to test our prediction. And it’s not uncommon to use a graph to display the values that we measure.

When we make a graph like this, typically on the horizontal axis, we put the independent variable. Recall that, for our experiment, that’s the shape of the rolling objects. And then the dependent variable goes on the vertical axis. That’s the time these objects take to reach the bottom of the ramp. On our horizontal axis then, we write out the object shapes: the sphere, cylinder, thin ring, and thick ring. And then we put the measured time to reach the bottom of the ramp in units of seconds on the vertical axis. And let’s say that each one of the tick marks we’ve put on this axis represents an additional second of time.

At this point, we’ll plot our values, that is, the measured quantities that we recorded. And let’s say that when we do that, those values look like this. So we can see that, according to our measurements, the sphere took just about four seconds to reach the bottom. The cylinder took a little bit less time than that. The thin ring apparently took over nine seconds to reach the bottom, while the thick ring took just over three.

Now notice how the times for all of our shapes are clustered between three and about four seconds, except for one, the thin ring. This value is a significant outlier from the rest of our data points. The name for this is anomaly. An anomaly is an unexpected result that doesn’t seem to fit within the pattern of the rest of the results. An anomaly may be but isn’t necessarily a sign of some experimental error.

To find out whether this value is truly legitimate, whether the thin ring really did take more than twice as long as any other shape to reach the bottom. We would want to test this anomaly by repeating our experiment. When we do, the results from that second run could either confirm or correct this anomalous result. Sometimes we hear of the importance of repeating a scientific experiment. And this is one of the reasons why. Repeating an experiment is a helpful technique for either confirming or rejecting anomalies.

Now that we’ve learned a number of terms and processes involved in making experimental measurements, let’s get some practice with these ideas through an example exercise.

Which of the following statements most correctly defines an independent experimental variable? A) An independent experimental variable is a quantity that cannot change in time. B) An independent experimental variable is a quantity that may unpredictably change in value during an experiment. C) An independent experimental variable is a quantity that does not change in value during an experiment. D) An independent experimental variable is a quantity that predictably changes in value during an experiment.

Okay, so we want to pick which of these four descriptions most correctly defines this term, independent experimental variable. We can recall that, in general, the purpose of a scientific experiment is to test a prediction that we make. Often this prediction takes a form like this. If 𝑋, where 𝑋 is some event or condition, then 𝑌, where 𝑌 is a result.

For example, we might make a prediction like this. If we pass light through an optical fiber, then the longer that fiber is, the less light will make it out the other end. Or another prediction could be. If we make the temperature inside a plant greenhouse hotter, then the plants inside it will grow faster. These predictions are both of the form “if 𝑋, then 𝑌.”

And it’s by looking at 𝑋, that first factor, that we come to understand the independent experimental variable in each case. For our experiment involving light traveling through an optical fiber, it’s the length of that fiber that’s the independent experimental variable. In the case of our plant greenhouse, it’s the temperature of the greenhouse that’s the independent variable. These are the factors that we vary in order to produce an expected result.

So an independent experimental variable is a quantity in an experiment that does change. It’s a variable, but it does so in a predictable or planned way. That is, it’s a quantity that we vary on purpose in order to investigate our prediction.

Knowing this, let’s review our answer choices, starting with option A. This option told us that an independent experimental variable is a quantity that cannot change in time. We see though that, for the success of our experiment, it’s important that our independent experimental variable does change.

Imagine that we try to test the prediction “If the temperature of a greenhouse is hotter, then the plants in it grow faster” without varying the temperature in the greenhouse. It wouldn’t work. We couldn’t test the prediction. So it’s vital that an independent experimental variable does change. This definition, that it’s a quantity that cannot change in time, sounds much more like a universal constant than it does an independent experimental variable. So we’ll cross option A off our list.

Option B says that an independent experimental variable is a quantity that may unpredictably change in value during an experiment. So this definition says that an independent variable can change — it may — but that it does so unpredictably. But this goes against our experimental design. How we plan out just how the independent experimental variable will change in order to test our prediction. This definition of a quantity that may unpredictably change in value during an experiment sounds more like an uncontrolled variable. An independent experimental variable is not uncontrolled. And so we’ll cross this choice off our list.

Choice C says that an independent experimental variable is a quantity that does not change in value during an experiment. But as we’ve seen, it’s important that the independent experimental variable does change in order to test our prediction. This description of a quantity that does not change in value during an experiment better describes a constant. We won’t choose option C then either.

Lastly, option D says that an independent experimental variable is a quantity that predictably changes in value during an experiment. This definition meets both of the conditions. That the independent experimental variable does change, that is, it truly is a variable, but that this change happens in an orderly or predictable way. That’s because as we design the experiment, we plan out just how this variable will change.

For example, in the case of our optical fiber experiment, we may plan specific lengths of fiber to use. Or in our greenhouse temperature experiment, we might plan specific temperatures to set the greenhouse to. All that to say the changes that occur for an independent experimental variable are indeed predictable. So the most correct definition is that an independent experimental variable is a quantity that predictably changes in value during an experiment.

Let’s summarize now what we’ve learned about experimental measurements. At the outset, we saw that scientific experiments are conducted in order to test predictions. Furthermore, experiments involve both variables as well as constants. We looked at four varieties of variables: independent, dependent, controlled, and uncontrolled. And we also studied two different types of constants. Simple constants, quantities that can change but don’t, and universal constants, which cannot change. And we saw that there’s a connection between constants and controlled variables, that they’re essentially the same thing. And lastly, we saw that anomalies are unexpected measured values that can be confirmed or corrected through repeating an experiment.

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