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 again are going to be the negative control and the positive control. The negative control, as its name implies, is the control group where no response is expected; it's expected to react negatively to the test, which is why it's called the negative control. For example, this would be like using a placebo, such as a sugar pill that isn't supposed to do anything at all. The purpose of using a negative control is to prevent false positives, which we defined in our last lesson video.
The positive control, by definition, as its name implies, is going to be the control group where a response is expected. It's 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 look at this image down below, which is an example of an experiment where they're testing a brand new experimental pill to see its drug effectiveness on a toe injury. When testing this experimental pill, it's important to include a negative control group and a positive control group. The negative control group would be something where we have expectations that it will not react; there will be no response. Using a sugar pill as a placebo, which is not supposed to do anything, is an example. So, if you take a sugar pill like this one right here, it's not supposed to help heal your toe injury. This expectation that the sugar pill will react negatively and will have very little drug response defines it as the negative control.
Over here, we have the positive control, and we have expectations that it will react positively. It will show a response. Using something like a brand name pill that has been 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.
Notice on the right-hand side, we have a graph where on the x-axis, we have the independent variables, which the scientists have control over, namely the exact type of pill they decide to use. On the y-axis, 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 show 0% drug effectiveness. If we were to actually use this sugar pill on a group and it showed that it had 100% drug effectiveness, then this would be an example of a false positive. Hence, including a sugar pill or a negative control group and having this group respond as expected is helping to prevent false positives. On the other hand, we have the brand name pill, which we said is going to be the positive control here because we have expectations that it should react positively and it should give some level of response. This proven effect in the past allows us to anticipate some level of drug effectiveness.
If this brand name pill were to somehow show 0% drug effectiveness, then that would be an example of a false negative. By including the brand name pill here and having it respond as expected, we're helping to prevent false negatives in our tests.
The experimental group here 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 to 100%. If it responded below the brand name pill, if it had less drug effectiveness, then perhaps the scientists would recommend not 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 perhaps the scientists would advise trying this experimental pill because it might help with your toe pain better than the brand-name pill. This demonstrates how positive and negative controls can be very helpful and 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.
