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1. Introduction to Biology

Experimental Design

1. Introduction to Biology

Experimental Design

1

concept

Variables

7m

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in this video, we're going to talk a little bit about experimental design as it applies to variables of an experiment. And so first we need to define what an experiment is. And so an experiment is defined as a scientific investigation or procedure that's designed to test the validity of a hypothesis or theory. Now a variable. On the other hand, as its name implies here with the root here of the word very, which means things that are going to change. Variables are changeable elements of the experiment, so they're going to change throughout the experiment. Now scientists will investigate the relationship between two main types of variables and an experiment and notice down below. In this table right here, we're going to introduce both of these types of variables, and so the very first variable here is going to be the independent. Variable on the second variable is going to be the dependent variable. Now the independent variable eyes going to be defined as the variable that is controlled or modified by the researcher. And so, for example, over here 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 decided to test on the middle age group of people. Or maybe they decide toe test on ah, younger group of people. So that's something that they can control or modify, so that could be an example of an independent variable. Now, another thing that the researcher could control or modify is the amount of time that someone or something is exposed to something. Uh, they could decide to expose them to something for a long period of time, or they could decide to expose them to something for a short period of time so the time or exposure can be controlled or modified by the researcher, and so this could be an example of an independent variable. And then, of course, the amount of, say, a chemical or something like that that the researcher decides to use is something that they can also control or modify. And so once again, these are just a few examples of things that could be independent variables because they can be controlled or modified by the researcher Now, on the other hand, when it comes to the dependent variable, they cannot be controlled or modified by the researcher. So instead, this is going to be defined as the variable that is measured or investigated by the researcher. So since they can't directly control it or modify it, what they do is instead they measure it to see how it changes throughout the experiment. So once again, over here in this column, we have some examples of what could be the dependent variable. It will change on an experiment by experiment basis, but 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 and something else could be like the drug effectiveness or the effectiveness of a drug. The researcher cannot directly control or modify it. So instead, what they do is they measure or investigate the effectiveness of the drug and so down below. What we have is an image of a specific experiment that is testing the effect of water on plant growth. And so when we take a look down below at this image, which will notice is we're showing you a graph over here and what you should note about this graph is that on the X axis here, the horizontal axis. Uh, normally, the scientists are going to put the independent variable, which is once again, uh, the variable that is controlled or modified by the researcher. Now, in this particular experiment of testing the effect of water on plant growth, what the researcher has control over is the amount of water that they use to water the plants. And so once again, you can see that amount can be the independent variable. And it is here in this particular example now on the Y axis, which is this vertical axis normally the dependent variable. It's gonna go here, which is the variable that's being measured by the researcher. And so, for this particular example of testing the effect of water on plant growth, the plant growth is going to be the dependent variable. And so what you can see is over here what we have eyes A little set up of the experiment notice that we have two identical plants on. Really. The only difference is that this plant is given little h 20 little water by the researcher. Whereas this plant over here is given Ah, high amount of H 20 or a lot of water by the researcher. And so what you'll see is that, uh the less water the plant receives, the less growth, the less plant growth we get. Whereas the mawr water we give the plant the mawr plant growth we get. And so what you might see is that when the amount of water is really, really low, which would be over here on this axis, we might expect to get very, very little growth. So we could put some, uh, data point over here. However, the mawr water we add, which would be over here on the x axis, uh, the mortgage growth we would expect to get. So we might expect to get a data point somewhere up here with the more water we get. And so you might expect to see a trend that looks something like this with this experiment on DSO. This is just some expected data here. It's not really data. It's just fake data for this example. But 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. And so this here concludes, our introduction to the different types of variables, and moving forward will be able to get a little bit of practice with this, so I'll see you guys in our next video.

2

Problem

Problem

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: _

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3

Problem

Problem

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.

4

Problem

Problem

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.

5

concept

False Positives/Negatives

1m

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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 will 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, ah, false positive is defined as an outcome that falsely indicates the presence of a result. So, 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, ah, false negative would be the opposite. This is going to be an outcome that falsely indicates the absence of a result. So, for example, if the pregnancy tests 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 tohave false negatives and false positives in their experiments. And so, 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.

6

concept

Negative & Positive Controls

7m

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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. And so really, there are two main types of controls that air used in experiments once again, the negative control and the positive control. And so, ideally, these control groups are on Lee going to differ from the experimental group in the one factor that's being tested. And so notice down below. In this little table here, we're going to distinguish between the two types of control the negative control and the positive control. And so, in the first column here, what we have is the control type, which once again are gonna be the negative control and the positive control. Now, the negative control, as its name implies with the negative here. By definition, it's gonna be the control group where no response is expected. And so it's expected to react negatively to the test. And that's why it's called the Negative Control. And so, for example, this would be like using something like a placebo, which is like, Ah, fake pill that's not supposed to do anything at all like a sugar pill, for instance. It shouldn't help with healing any kind of injury. Now the purpose of using a negative control would be to prevent false positives, which we defined what false positives are in our last lesson video. Now the positive control, on the other hand, by definition, as its name implies, is going to be the control group where a response is expected. So it's supposed to react positively to the test, and that's why it's called the Positive Control. And this would be, for example, using something like a brand name pill that has been proven to work successfully in the past. Now the purpose of using a positive control would be to prevent false negatives. So if we take a look at this image down below, what we have is an example of an experiment where they're testing this brand new experimental pill to see its drug effectiveness on the toe injury here. Now, if we are testing this experimental pill, that's brand new on how well it's drug effectiveness is on healing this toe injury. We might want to include a negative control group and a positive control group. Now the negative control group would be something where we have expectations that it will not react. There will be no response. And so, for instance, using something like a sugar pill would be an example of using a placebo, something that is a fake pill and is not supposed to do anything. So if you eat a sugar pill like this one right here, it's not supposed to help heal your toe injury. And so because we have expectations that the sugar pill is going to react negatively and we'll have very, very little drug response Uh, that is the negative control. Now, over here, what we have is the positive control on the positive control. We have expectations that it will react positively, positively. It will show a response. And so, using something like a brand name pill that has been proven to be successful with helping with toe injuries would be an example of a positive control, because once again we have expectations that it should react positively and it should have some level of drug effectiveness. Now notice. Over here on the right hand side, what we have is a graph. Where on the X axis over here. What we have is the independent variables which the scientists have control over on that is going to be the exact type of pill that they decide to use. And then on the y axis of this graph, what we have is 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. Now, once again, the sugar pill here is going to be our negative control because we have expectations that it should react negatively. So it should not help with the toe injury at all. And it should be 0% drug effectiveness. This is what it should be now. If we were to actually use this sugar pill on a group and it was to show that it had, ah 100% drug effectiveness, then this would be an example of a false positive. And so, by including a sugar pill, a negative control group, and having this negative control group respond, as is expected, that is helping to prevent false positives. Now, on the other hand, over here, what we have is the brand name pill, which we said is gonna be the positive control here because we have expectations that it should react positively and it should give some level of response, maybe not 100%. But it should give some level because in the past it's been proven toe work successfully on helping toe pain. And so once again we would expect some level of drug effectiveness. Let's just say somewhere around here, and so that's the expectation Now, if this brand named Pill were to somehow have 0% drug effectiveness, then that would be an example of a false negative. And so, by including the brand named Pill here and having it respond as expected, we're helping to prevent false negatives in our test. And that's exactly what we said here, where the purpose of the negative control to prevent false positives and the positive control to prevent false negatives. Now the experimental group here would be the brand new experimental pill that we're testing for maybe the first time. And so this experimental pill might respond, Uh, in any level, it could respond anywhere from here all the way up to 100% now if it responded below the experimental. I'm sorry if it responded below the brand name pill. If it had less drug effectiveness, then maybe the scientists would be like, Hey, don't use the experimental pill. It's not as good as the brand name pill. But if the experimental pill responded better with better drug effectiveness, then maybe the scientists would say, Hey, try this experimental pill because it might help with your toe pain better than the brand name pill over here. And so you can see here how positive and negative controls can be very, very helpful and very useful for a scientist. And so this year concludes our introduction to the difference between negative and positive controls, and we'll be able to get some practice moving forward and our course, so I'll see you guys in our next video.

7

example

Experimental Design Example 1

2m

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All right. 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 one s so that it matches with number one over here, and then we can cross off one from our list. Now moving on the second description here says, Ah, 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 tohave a positive result. And so this would be an example off a false negative. And so that would match with D down here so we could put to here and then cross off to from this list. Now three here says a brand name pill that has proven toe work on patients. And so once again, we have an expectation that this brand name pill should react positively. It should work. And, uh, because we expect that it should react positively, This would make it the positive control. And so we can put number three here and cross three off our list. And then, of course, last but not least, the false positive is gonna be number four, which says, Ah, 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 gonna match with four 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.

8

Problem

Problem

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.

Additional resources for Experimental Design

PRACTICE PROBLEMS AND ACTIVITIES (13)