Experimental Design - Video Tutorials & Practice Problems
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1
concept
Variables
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7m
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Video transcript
In this video we're going to talk about experimental design by focusing on the variables within an experiment. And so first we need to define what an experiment actually is. And so an experiment can be defined as a scientific investigation or procedure that's designed to test the validity of a hypothesis or a theory. And so within a well designed experiment, there will be variables. And so as its name implies, variables has this root of the word very and so it is going to involve changes that vary over time and so variables are actually changeable elements of the experiment and so these variables will change throughout the course of the experiment. Now, scientists will typically investigate the relationship between 2 main types of variables. And so down below in this table, we're going to introduce those 2 main types of variables. And so, the first type of variable that scientists typically investigate is going to be the independent variable and the second variable is going to be the dependent variable. Now, the independent variable is again a variable which means it is going to change throughout the course of the experiment. However, the independent variable is a variable that is going to be controlled or modified by the researcher. And so over here we have a few potential examples of what could be an independent variable in an experiment. And so, one potential example is the age group of the people involved in the experiment and so the researcher can control or modify the age group that they focus their experiment on. Perhaps the researcher chooses to focus their experiment on the elderly group of people or perhaps they focus on the middle age group of people or perhaps they focus on the younger group of people. And so because the age can be controlled or modified by the researcher, it could potentially be an independent variable in an experiment. Now another potential example is the amount of time that someone is exposed to, something. So perhaps, the scientist will choose to expose, the group to a chemical for a long period of time or perhaps they will choose to expose, for a short period of time, but the time is something that can be controlled or modified by the researcher and so it could potentially be an independent variable in an experiment. And last but not least, another potential example of an independent variable is the amount of say a specific reagent that is used. Again that can be controlled or modified by the researcher and so it could be an independent variable. Now, the dependent variable on the other hand cannot be controlled or modified by the researcher and so instead of trying to control or modify this variable, the researchers will instead just measure or investigate this variable. And so the dependent variable can be defined as the variable that is going to be measured or investigated by the researcher since they cannot control or modify it directly. And so over here we have a few potential examples of dependent variables for an experiment and one of them could be perhaps the growth of a plant and so a scientist cannot directly will be a potential, dependent variable. Will be a potential, dependent variable. Another potential example is the drug effectiveness or the effectiveness of a drug. Now a researcher cannot directly control or modify the effectiveness of a drug so instead of trying to control or modify it they will simply measure the drug effectiveness making it a potential dependent variable. And so down below we're going to take a look at an experiment that is testing the effect of water on plant growth and we're going to analyze the independent and dependent variables here. And so what you'll notice is that, with a typical graph the independent variable is usually going to be on the x axis, the, horizontal axis of the graph. And the independent variable again is going to be the variable that is controlled or modified by the researcher. And in this experiment, what you'll notice is that we've got these 2 plants, and both of them are receiving sunlight, the same amount of sunlight but they are receiving different amounts of water. This plant here is receiving a little h two o or a little amount of water and this plant over here is receiving high h two o or a lot of water. And so, because the scientist is controlling and modifying the amount of water and, the amount of water is different, then we can say that the amount of water here is going to be the d, the independent variable. So the independent variable will be the amount of water because that is being controlled or modified by the researcher and so once again you can see amount can be an independent variable. Now, in this example the dependent variable is going to be plant growth and usually the dependent variable is going to be on the y axis of the graph or the vertical axis of a graph. And so what you'll notice here, in this experiment is the plant that received little water, did not grow nearly as much as the plant that received a lot of water. And so what we're seeing here is that when there is little water or over here on the x axis, there is not a lot of growth so we would expect a data point somewhere around here. But when there is a lot of water somewhere over here on the axis, there is a lot of plant growth. So we would expect the data point somewhere around here And so we would expect a trend to be something like this. And so, this here would be a graph that shows the relationship between the independent variable, the amount of water which is again controlled or modified by the researcher, and, its impact on the dependent variable which is, the variable that is going to be measured by the researcher and that is going to be the plant growth, and so this here concludes our brief lesson on the variables within an experiment and we'll be able to get some practice applying these concepts as we move forward. So I'll see you all 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?
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
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1m
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Video transcript
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. 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, a 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 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. 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
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7m
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Video transcript
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 2 main types of controls that are used in experiments. Once again the negative control and the positive control. And so ideally these control groups are only 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 a 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, and how well, its 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 gonna react negatively and will have very very little drug response, that is the negative control. Now over here what we have is the positive control and 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, and that is gonna 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 a 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 a 100%, but it should give some level because in the past it's been proven to work successfully on helping topain. 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 name pill were to somehow have 0% drug effectiveness, then that would be an example of a false negative. And so by including the brand name 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 in any level. It could respond anywhere from here all the way up to a 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 here 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. So I'll see you guys in our next video.
7
example
Experimental Design Example 1
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2m
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Video transcript
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 gonna 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 gonna 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.
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?