You will see the term type 1 error, or alpha error, come up when you are talking about hypothesis testing. This article will discuss what is meant by a type 1 error, its implications, and how to minimize the impact of making this type of error.
Overview: What is a type 1 error?
Before you can fully understand what is meant by a type 1 error, we should do a little review of hypothesis testing. In hypothesis testing, you state some assumptions about your data, and then go about trying to prove whether they are true or not. In all cases, the first thing you do is state the null and alternate or alternative hypotheses. The word null in the context of hypothesis testing means nothing or zero.
For example, your null hypothesis might be there is no difference in average turnaround time between your current and new invoicing processes. Your alternate hypothesis would say there is a difference. Since you’re using sample data to make your inferences about the population, there is a risk you’ll make an error. In the case of the null hypothesis, you have a risk of making one of two errors.
- Type 1 error, also known as an alpha error or producer error: A type 1 error is when you mistakenly reject the null and believe something significant happened when it didn’t.
- Type 2 error, also known as a beta error or consumer error: A type 2 error is when you fail to reject the null when you should have.
The decision whether to reject your null is guided by something called the p-value, which is calculated from the data. The p-value is the actual risk you have in being wrong if you reject the null. You would like that to be low.
An industry example of a type 1 error
The company Master Black Belt (MBB) did a hypothesis test on whether a retrofitted machine was faster than the current configuration. He had hoped if it was, he could recommend retrofitting the other 20 machines. After collecting a small amount of data, he rejected the null hypothesis and stated the retrofitted machine was faster. He then recommended the other machines also be retrofitted.
Unfortunately, after 10 more were retrofitted and data collected, he realized there was no statistically significant difference in speed and that he had made a type 1 error. The company stopped retrofitting the remaining machines and wrote off the wasted money spent on doing the first 10.
Frequently Asked Questions (FAQ) about a type 1 error
What’s the difference between a type 1 and type 2 error?
A type 1 error is when you incorrectly reject the null hypothesis and claim something significant happened when it didn’t. A type 2 error will occur when you fail to reject the null hypothesis when something significant happened thereby erroneously claiming nothing significant happened when it did.
What is an alpha error?
Alpha error is another term for a type 1 error.
Which is worse, a type 1 or type 2 error?
It depends on the situation. In the business environment, a type 1 error will usually cost you money. For example, you concluded your new marketing program worked so you rolled it out only to discover it didn’t. This will cost you money. On the other hand, if you conclude the new marketing program was not successful (type 2 error) and fail to roll it out, you will incur an opportunity cost. But, that does not preclude you from rolling it out later.
