What is Type I Error?
A Type I error is a statistical error that occurs when you reject a null hypothesis when it is true. In other words, you make a false positive. This error can occur when you are testing for a difference between two groups when there is no difference.
The definition of Type I Error
Type I Error concludes that there is a difference when there is none. Put another way, Type I Error is committed when we reject a null hypothesis when it is actually true. The more extreme the difference we try to detect, the greater the chance of committing a Type I Error.
In statistical hypothesis testing, we start out with a null hypothesis, which we then attempt to disprove through our analysis. If we are able to disprove the null hypothesis, then we have “proven” that there is a difference. However, if our analysis fails to disprove the null hypothesis, then all we can say is that we were unable to find a difference. We cannot say for sure that there is no difference, only that we did not find one. This last point is important: just because we did not find a difference does not mean that there isn’t one!
Suppose that you are testing for a new weight-loss drug. The null hypothesis would be that there is no difference between the new drug and placebo (i.e., taking no drug at all). The alternative hypothesis would be that there is a difference between the two. If your analysis shows that there is indeed a significant difference between the two groups, then you have “proven” (at least to some degree) that the new drug works. However, if your analysis fails to show a significant difference, all you can say is that you were unable to find enough evidence to support the claim that the new drug works. You cannot say for sure that it doesn’t work; you can only say that you didn’t find enough evidence to support the claim.
The probability of committing a Type I Error (i.e., rejecting a true null hypothesis) is known as alpha (α). Alpha is usually set at 0.05, which means that there is only a 5% chance of commit- ting a Type I Error if the null hypothesis is true.
The formula for Type I Error
Type I Error is the probability of falsely rejecting the null hypothesis when it is true. In other words, it is the probability of concluding that there is a difference when there really isn’t. The formula for Type I Error is:
P(Type I Error) = P(Rejecting H0|H0 is true)
The example of Type I Error
In statistics, a type I error is when a researcher rejects the null hypothesis when it is actually true. The null hypothesis is the statement being tested. For example, if the null hypothesis states that there is no difference between two treatment groups and the researcher finds a significant difference, then a type I error has occurred.
Type I errors are also known as false positives. They can be costly because they cause researchers to make decisions based on incorrect information. Type I errors can also lead to wasted resources and missed opportunities. For example, if a drug company spends millions of dollars developing a new drug that turns out to be no better than existing treatments, this would be a type I error.
How to avoid Type I Error
Type I Error is when you reject the null hypothesis when it is actually true. You can avoid Type I Error by using a stricter criterion for rejecting the null hypothesis.
The ways to avoid Type I Error
There are several ways to avoid Type I Error:
-Identify the risks and allocate resources accordingly
-Communicate the risks and their significance to those who need to know
-Use sound methods and tools to reduce the risk of error
-Be prepared to accept responsibility for any errors that do occur
The precautions for Type I Error
A Type I error is the incorrect rejection of a true null hypothesis. A Type I error occurs when we believe that a difference exists when there is no actual difference. Put another way, a Type I error occurs when our results indicate that a difference exists when, in truth, no difference exists.
It is important to understand that, in general, the probability of making a Type I error (falsely rejecting the null hypothesis) is equal to the level of significance (α) that we set for our test. For example, if we set α = 0.05 and conduct our test, there is approximately a 5% chance that we will make a Type I error. If we set α = 0.01 and conduct our test, there is approximately a 1% chance that we will make a Type I error.
There are several things that we can do to reduce the likelihood of making a Type I error:
- Use a larger sample size: This will increase the power of our test and decrease the likelihood of making a Type I error.
- Use a more powerful statistical test: This will also increase the power of our test and decrease the likelihood of making a Type I error.
- Set a stricter level of significance: This will decrease the probability of making a Type I error but could also lead to an increase in the probability of making a Type II error (which we will discuss next).