In statistical hypothesis testing, a Type I error is essentially the rejection of the true null hypothesis. The type I error is also known as the false positive error. In other words, it falsely infers the existence of a phenomenon that does not exist.
Note that the type I error does not imply that we erroneously accept the alternative hypothesis of an experiment.
The probability of committing the type I error is measured by the significance level (α) of a hypothesis test. The significance level indicates the probability of erroneously rejecting the true null hypothesis. For instance, a significance level of 0.05 reveals that there is a 5% probability of rejecting the true null hypothesis.
How to Avoid a Type I Error?
It is not possible to completely eliminate the probability of a type I error in hypothesis testing. However, there are opportunities to minimize the risks of obtaining results that contain a type I error.
One of the most common approaches to minimizing the probability of getting a false positive error is to minimize the significance level of a hypothesis test. Since the significance level is chosen by a researcher, the level can be changed. For example, the significance level can be minimized to 1% (0.01). This indicates that there is a 1% probability of incorrectly rejecting the null hypothesis.
However, lowering the significance level may lead to a situation wherein the results of the hypothesis test may not capture the true parameter or the true difference of the test.
Example of a Type I Error
Sam is a financial analyst. He runs a hypothesis test to discover whether there is a difference in the average price changes for large-cap and small-cap stocks.
In the test, Sam assumes that the null hypothesis is that there is no difference in the average price changes between large-cap and small-cap stocks. Thus, his alternative hypothesis states that the difference between the average price changes does exist.
For the significance level, Sam chooses 5%. This means that there is a 5% probability that his test will reject the null hypothesis when it is actually true.
If Sam’s test incurs a type I error, the results of the test will indicate that the difference in the average price changes between large-cap and small-cap stocks exists while there is no significant difference among the groups.
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