Source: Image of blood test, PD, http://www.clker.com/clipart-3188.html; Image of bed, PD, http://www.clker.com/clipart-bed-1.html; Image of tape measure, PD, http://www.clker.com/clipart-10170.html; Image of house, PD, http://www.clker.com/clipart-gingerbread-house.html; Image of bricks, PD, http://www.clker.com/clipart-1771.html; Image of table created by Dan Laub
[MUSIC PLAYING] Hi, Dan Laub here. And in this lesson, we're going to discuss Type I and Type II errors. But before we do so, let's cover the objective for the lesson.
By the end of this lesson, you should be able to identify Type I and Type II errors and then know what the difference is between them. So let's get started.
Recall that when it comes to the experimental method, the null hypothesis states that there is not a cause and effect relationship that exists between two variables. Whereas an alternative hypothesis states that there might be some cause and effect relationship between these variables. When there is an actual cause and effect relationship that exists between two variables, one expects to get a significant result from an experiment.
While one may carefully perform experiments in order to establish whether or not these two variables are related, there is still a need to be careful in the event that an error might come from the results of the experiment. The two major errors that are possible when conducting experiments are rejecting the null hypothesis, when it, in fact, is actually true, and failing to reject the null hypothesis when it is actually wrong.
In the case when two variables are not actually related in terms of a cause and effect relationship, it is still possible to see a significant result from an experiment. An example of such errors exists when a doctor orders a lab to conduct blood work for a specific medical condition for a specific patient.
Since the test is unlikely to be perfect, there exists a chance that the person interpreting the results rejects the null hypothesis when they shouldn't. In a case like this, the null hypothesis is that the patient does not have the condition, which means that the test is erroneously interpreted as the patient having the condition. This is also known as a Type I error, or a false positive. In a case like this, it is possible that the error could lead to a misdiagnosis, in which they are then treated for a condition that they simply don't have.
On the flip side, it is possible that the blood test is not rejected when, in fact, it should be. This will be known as a false negative. And in this situation, it could lead to a lack of a diagnosis where the patient actually has a condition but winds up not being treated for it due to such an error. An error like this is referred to as a Type II error, or a false negative.
As this example clearly illustrates, it is very helpful for a researcher to know if they run the risk of committing either type of error, as it can have a major impact on whether or not the results are being communicated accurately.
So let's consider two variables, such as an individual's height and the number of hours they sleep per night on average. A null hypothesis in this instance states that there is not a cause and effect relationship between height and the amount someone sleeps. And one would clearly expect the null hypothesis to be true in this case.
Suppose that a researcher conducted a study that showed a result as being significant. This will be a clear indicator that the variables are related, when in fact, they really aren't, and is an example of a Type I error.
In a case like this, such an error would lead the researcher to believe that the sleeping habits of people may in fact be influenced by how tall they actually are.
In the case, of two variables such as the size of a home and the amount of materials required to build it, there is a clear cause and effect relationship that exist. Obviously larger homes require a lot more building materials than smaller homes do. Here, the null hypothesis would state that larger homes do not require more building materials and smaller homes, which is obviously incorrect in the situation.
Suppose that another researcher conducted an experiment to test this relationship and discovered that the results were not significant. This would be a possible indicator that a Type II error occurred, or a false negative, and could lead to the conclusion that larger homes don't actually take more building materials to construct, something which could turn out to be quite costly to a builder.
In summary of this lesson, the table you see here provides a brief rundown of the differences between Type I and Type II errors.
So let's go back to our objectives just to make sure that we covered what we said we would. We wanted to be able to identify Type I and Type II errors, which we did, and to identify the difference between the two of them, which we did as well, knowing that one is a false positive, which is a type I error, and the other is a false negative, which is a Type II error.
So again, my name is Dan Laub. And hopefully, you got some value from this lesson.