Experimental Design  - Online Tutor, Practice Problems & Exam Prep

Experimental design involves understanding independent and dependent variables. The independent variable is manipulated by the researcher, such as the amount of water given to plants, while the dependent variable is measured, like plant growth. Control groups are essential to prevent false positives and negatives, with negative controls showing no expected response and positive controls showing expected responses. This ensures accurate results in experiments, such as testing drug effectiveness, where the outcomes guide future applications and hypotheses in scientific research.

Variables

In this video, we're going to talk a little bit about experimental design as it applies to variables of an experiment. First, we need to define what an experiment is. An experiment is defined as a scientific investigation or procedure that's designed to test the validity of a hypothesis or theory. A variable, on the other hand, as its name implies with the root of the word 'vary', means things that are going to change. Variables are changeable elements of the experiment, so they're going to change throughout the experiment. Scientists will investigate the relationship between 2 main types of variables in an experiment.

We're going to introduce both of these types of variables. The first variable here is the independent variable, and the second variable is the dependent variable. The independent variable is defined as the variable that is controlled or modified by the researcher. For example, in this column, we have a few examples that could be independent variables. For instance, the age group of the people used in the experiment can be controlled or modified by the researcher. Maybe the researcher decides to test on the elderly group of people, maybe they decide to test on the middle age group of people, or maybe they decide to test on a younger group of people. So that's something they can control or modify, which could be an example of an independent variable.

Another thing that the researcher could control or modify is the amount of time that someone or something is exposed to something. They could decide to expose them for a long period of time or for a short period of time. So the time or exposure can be controlled or modified by the researcher, and this could be an example of an independent variable. The amount of a chemical that the researcher decides to use is something that they can also control or modify, and these are just a few examples of things that could be independent variables because they can be controlled or modified by the researcher.

On the other hand, when it comes to the dependent variable, they cannot be controlled or investigated by the researcher. Since they can't directly control it or modify it, what they do is measure it to see how it changes throughout the experiment. For example, in this column, we have some examples of what could be the dependent variable. It will change on an experiment by experiment basis. Something like the growth of the plant would be an example of a dependent variable. The researcher cannot directly control the growth of the plant. So instead, what they do is they measure the growth of the plant to see how it changes over time. The effectiveness of a drug is another dependent variable. The researcher cannot directly control or modify it. So instead, what they do is measure or investigate the effectiveness of the drug.

We have an image of a specific experiment that is testing the effect of water on plant growth. In this graph, on the x-axis, which is the horizontal axis, the scientist normally puts the independent variable, which is once again the variable that is controlled or modified by the researcher. In this experimental example of testing the effect of water on plant growth, the researcher has control over the amount of water they use to water the plants. This amount can be the independent variable.

On the y-axis, which is the vertical axis, normally the dependent variable goes here, which is the variable that is being measured by the researcher. For this particular example, plant growth is going to be the dependent variable. Notice that we have 2 identical plants, and the only difference is that this plant is given little H₂O (water) by the researcher, whereas this plant over here is given a high amount of H₂O or a lot of water by the researcher. The less water the plant receives, the less plant growth we get. Whereas the more water we give the plant, the more plant growth we get.

You might expect to see a trend that looks something like this, with this experiment. This is just some expected data here. It's not real data, it's just fake data for this example. The idea here is to really distinguish between the two types of variables: the independent variable, which the researcher controls and modifies, and the dependent variable, which the researcher measures. This concludes our introduction to the different types of variables and moving forward, we'll be able to get a little bit of practice with this. So I'll see you guys in our next video.

Jonathan wants to know which style/model of paper airplane is going to win the contest by traveling the furthest. He designs 5 different models of paper airplanes and drops each of them from the same height of 20 meters. He records the distance that each plane travels before it hits the ground. What are the independent and dependent variables of Jonathan's experiment?

            Independent Variable:______________________________________ 

            Dependent Variable:_______________________________________

Alright. So here we have a practice problem that says, Jonathan wants to know which style or model of paper airplane is going to win the contest by traveling the farthest. He designs 5 different models of paper airplanes and drops each of them from the same height of 20 meters. He then records the distance that each plane travels before it hits the ground. What are the independent and the dependent variables of Jonathan's experiment? And so you can see that we can put the independent variable here in this blank and the dependent variable here in this blank. And so what we need to recall from our last lesson video is that the independent variable is going to be the variable that is controlled or modified by the researcher, and the researcher here is going to be Jonathan himself. And of course, the dependent variable on the other hand is going to be the variable that is measured by the researcher. And so when we go back and revisit this, practice problem up above, what you'll realize is what Jonathan has complete control over are the styles, the different models of paper airplanes, and so there are exactly 5 of them, and he can control exactly which models he decides to use. And so because he has complete control over this variable and he can change it as he pleases, we can say that the independent variable is going to be the different models of airplanes. And so that is it for the independent variable, the different models of paper airplanes. Now when it comes to the dependent variable on the other hand, what Jonathan will be measuring is the distance that each plane travels before it hits the ground, and so that would be the dependent variable. So what we'll say is the distance traveled by each airplane. That is what he's going to measure. He can't really physically control how far each of these is going to travel. So instead what he does is he measures that and he investigates that. And so really, these here are the answers to this practice problem and that concludes this practice so I'll see you guys in our next video.

In an experiment to test the effect of temperature bacterial reproduction rate, temperature would be the:

• A. Standardized variable.
• B. Dependent variable.
• C. Control variable.
• D. Independent variable.

The temperature at which an alligator's egg is incubated will determine the sex of the offspring. The dependent and the independent variables in this experiment are ________.

• A. Sex of the baby alligator and temperature respectively.
• B. Temperature and sex of the baby alligator respectively.
• C. Size of the incubator and size of the baby alligator respectively.
• D. Number of offspring and temperature in the incubator respectively.

False Positives/Negatives

In this video, we're going to talk about false positives and false negatives. And so what you guys should know is that a very well-designed experiment is going to contain control groups. Now, in our next lesson video, we'll talk a lot more about these control groups, but for now, what you should know is that the control groups that scientists use in a well-designed experiment are specifically supposed to prevent false positives and false negatives.

So, what are these false positives and false negatives? Well, a false positive is defined as an outcome that falsely indicates the presence of a result. For example, if someone were to take a pregnancy test and that pregnancy test were to say that they are pregnant when in reality they actually are not pregnant, that would be a false positive. Now, on the other hand, a false negative would be the opposite. This is going to be an outcome that falsely indicates the absence of a result. For example, if the pregnancy test were to say that you're not pregnant when in reality you actually are pregnant, that would be a false negative.

And so, really, false positives and false negatives are bad, and scientists do not want to have false negatives and false positives in their experiments. In order to avoid or prevent false positives and negatives, scientists use control groups. And once again, we'll talk more about these control groups in our next lesson video. So, I'll see you guys there.

Negative & Positive Controls

In this video, we're going to talk about how scientists avoid or prevent false positives and false negatives in their experiments by using negative and positive controls. There are two main types of controls that are used in experiments: the negative control and the positive control. Ideally, these control groups will only differ from the experimental group in the one factor that's being tested. Let's distinguish between the two types of control: the negative control and the positive control.

In the first column, we have the control type, which are the negative control and the positive control. The negative control, as its name implies, is the control group where no response is expected. It is expected to react negatively to the test, and that's why it's called the negative control. For example, this would be like using something like a placebo, which is a fake pill that's not supposed to do anything at all, like a sugar pill. The purpose of using a negative control is to prevent false positives.

The positive control, on the other hand, is the control group where a response is expected. It is supposed to react positively to the test, and that's why it's called the positive control. This would be, for example, using something like a brand name pill that has been proven to work successfully in the past. The purpose of using a positive control is to prevent false negatives.

Let's consider an experiment where scientists are testing a new experimental pill to see its drug effectiveness on a toe injury. If we are testing this experimental pill's drug effectiveness on healing this toe injury, we might want to include a negative control group and a positive control group. The negative control group would be something like a sugar pill, a placebo that is not supposed to do anything. So, if you consume a sugar pill like this, it's not supposed to help heal your toe injury. Because we have expectations that the sugar pill will react negatively and will have very little drug response, that is the negative control.

In the positive control, we have expectations that it will react positively. It will show a response, and so using something like a brand-name pill that has been proven to be successful in helping with toe injuries would be an example of a positive control because we have expectations that it should react positively and it should have some level of drug effectiveness.

Over here on the right-hand side, what we have is a graph where on the x-axis, we have the independent variables, which the scientists have control over, and that is going to be the exact type of pill that they decide to use. On the y-axis of this graph, we have the dependent variable, which is what the scientists are going to measure, which would be the drug effectiveness and how good it is at healing the toe injuries.

The sugar pill here is going to be our negative control because we have expectations that it should react negatively. It should not help with the toe injury at all and it should have 0% drug effectiveness. If this sugar pill were to show that it had 100% drug effectiveness, then this would be an example of a false positive. By including a sugar pill as a negative control group and having this group respond as expected, that helps to prevent false positives.

On the other hand, the brand name pill, which is our positive control here because we have expectations that it should react positively, and it should give some level of response, maybe not a 100% but some level because, in the past, it's been proven to work successfully on helping to heal toe pain.

In this test, the experimental group would be the brand new experimental pill that we're testing for maybe the first time, and this experimental pill might respond at any level, from 0% up to 100%. If it responded below the brand name pill, maybe the scientists would advise against using the experimental pill, as it's not as good as the brand name pill. But if the experimental pill responded better, with better drug effectiveness, then the scientists might recommend this experimental pill because it might help with your toe pain better than the brand name pill. Positive and negative controls can be very helpful and very useful for a scientist.

This concludes our introduction to the difference between negative and positive controls, and we'll be able to get some practice moving forward in our course. See you guys in our next video.

Experimental Design Example 1

Alright. So here we have an example problem that says a scientific researcher designs an experiment to test the effectiveness of a new sleeping pill compared to the brand name product. Match each of the following controls or outcomes in the experiment, which we have over here, to their appropriate description which we have over here. And so notice that the first description here says a sugar pill that should have no effect on the patient.

And so notice that we have an expectation that this sugar pill should have no effect or it should respond negatively to the test. And so because we have an expectation that it should respond negatively this would be our negative control. And so over here in this blank we can put number 1, so that it matches with number 1 over here and then we can cross off 1 from our list. Now moving on, the second description here says a patient does not fall asleep after taking the brand name pill.

Now remember that the brand name pill has been proven to be successful in the past and so we have expectations that it should react positively. And so if it does not react positively, remember the brand name pill is supposed to be a sleeping pill, so it's supposed to put people to sleep. And so if the patient does not fall asleep, that means that we're getting a negative result even when we're supposed to have a positive result, and so this would be an example of a false negative and so that would match with d down here so we could put 2 here and then cross off 2 from this list. Now 3 here says a brand name pill that has proven to work on patients,

And so once again we have an expectation that this brand name pill should react positively, it should work and because we expect that it should react positively this would make it the positive control and so we can put number 3 here and cross 3 off our list. And then of course, last but not least the false positive is going to be number 4 which says a patient falls asleep after taking the non-effective sugar pill.

And the sugar pill here would be like the placebo, which would be the pill that is not supposed to have any effect since it's like a fake pill. And so this is going to match with 4 being a false positive. That the patient falls asleep, that's the positive result when they're not supposed to fall asleep. And so, here are the answers to this example problem and that concludes this example, so I'll see you in our next video.

A scientist wants to study the effects of nitrogen on wheat plants. They set up an experiment with 4 groups of plants:group A gets 20 pounds of nitrogen per acre, group B gets 40 pounds per acre, group C gets 60 pounds per acre, and group D gets 0 pounds per acre. Which of the following is the control group? Is it a positive or negative control group?

• A. Group A.
• B. Group B.
• C. Group C.
• D. Group D.

Here’s what students ask on this topic:

What is the difference between independent and dependent variables in experimental design?

In experimental design, independent variables are those that the researcher manipulates or controls. For example, the amount of water given to plants in a growth experiment. Dependent variables, on the other hand, are the outcomes that are measured to see how they respond to changes in the independent variable. For instance, in the same plant growth experiment, the plant growth itself would be the dependent variable. The independent variable is plotted on the x-axis, while the dependent variable is plotted on the y-axis of a graph. Understanding these variables is crucial for designing valid experiments and interpreting results accurately.

How do control groups help prevent false positives and false negatives in experiments?

Control groups are essential in experimental design to prevent false positives and false negatives. A negative control group is expected to show no response, helping to identify false positives, which are incorrect indications of a result. For example, a placebo in a drug trial serves as a negative control. A positive control group, expected to show a response, helps identify false negatives, which are incorrect indications of no result. For instance, a proven drug in a trial serves as a positive control. By comparing the experimental group to these controls, researchers can ensure the accuracy and reliability of their results.

What are false positives and false negatives in the context of experimental design?

In experimental design, a false positive is an outcome that incorrectly indicates the presence of a result. For example, a pregnancy test showing a positive result when the person is not pregnant. A false negative, conversely, is an outcome that incorrectly indicates the absence of a result, such as a pregnancy test showing a negative result when the person is actually pregnant. Both false positives and false negatives can lead to incorrect conclusions, making it crucial to design experiments with appropriate control groups to minimize these errors.

Why are positive and negative controls important in scientific experiments?

Positive and negative controls are crucial in scientific experiments to ensure the validity and reliability of the results. A negative control, which is not expected to show a response, helps identify false positives. For example, a placebo in a drug trial serves as a negative control. A positive control, expected to show a response, helps identify false negatives. For instance, a proven drug in a trial serves as a positive control. By comparing the experimental group to these controls, researchers can accurately determine the effect of the independent variable on the dependent variable, ensuring the experiment's accuracy.

How do you identify independent and dependent variables in an experiment?

To identify independent and dependent variables in an experiment, first determine what the researcher is manipulating or controlling. This is the independent variable, such as the amount of water given to plants. Next, identify what is being measured or observed in response to changes in the independent variable. This is the dependent variable, such as the growth of the plants. The independent variable is typically plotted on the x-axis, while the dependent variable is plotted on the y-axis of a graph. Understanding these variables is essential for designing valid experiments and interpreting results accurately.