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.
