Type I Error

Type I error is a phenomenon in statistics when the null hypothesis is mistakenly rejected when it is actually true. It occurs while conducting hypothesis testing. This error is commonly known as a false positive outcome as it falsely concludes the occurrence of an event that has not actually transpired.

These errors are considered errors of commission because, in this error type, researchers inaccurately conclude something as a fact. However, this error does not necessarily mean that the researchers mistakenly accept the alternative hypothesis in the test. It is sometimes called an error of the first kind.

• Type I error is a type of error that represents a scenario where the researchers reject the null hypothesis by mistake when it is actually true.
• It occurs while running a hypothesis test.
• The error is also known as a false positive conclusion, as it wrongly concludes that an event has occurred when it did not.
• This error can be a result of random sampling methods where the data is insufficient to reach a proper conclusion. Additionally, using incorrect research methods may result in the occurrence of this error.

Type I error is a statistical concept that represents the false positive conclusion in a hypothesis testing that occurs when the researchers erroneously reject the null hypothesis when it is actually true. This error type inaccurately concludes that an event has occurred when, in reality, it did not.

The possibility of encountering this error is evaluated by the significance level (α) of the hypothesis test. The level of significance in the hypothesis testing represents the chances of the researchers mistakenly rejecting the null hypothesis when it is true. For instance, a significance level of 0.01 indicates that the probability of the researcher rejecting the true null hypothesis is 1 percent. This error is considered an error of commission where researchers incorrectly conclude an event as an actual occurrence.

The Type I error causes are as follows:

• Random sample: No random chance is capable of accurately representing the population it aims to describe. Researchers generally sample a small part of the entire population. Thus, the outcomes may not accurately represent or predict reality. The findings may be a product of random odds.
• Improper research methods: While conducting a test, it is necessary to collect adequate data to reach the expected level of statistical significance. Researchers may initiate running a test and conclude it when they feel that there is a clear outcome despite not gathering sufficient data to reach their desired level of statistical significance. This scenario is one of the most common Type I error causes.

Let us study the following Type I error examples to understand this concept:

Example #1

Suppose Jake is a bank employee who ran a hypothesis test to determine if a particular customer should be provided a loan. The customer had a credit score that was above the minimum threshold. The null hypothesis suggested that the customer would not default on the loan. The alternative hypothesis suggested that they would default. However, the test encountered an error, and the customer defaulted on the loan. This is a Type I error example.

Example #2

Adaptive clinical trials are getting increasingly popular because they are more adaptable, practical, and ethical than the traditional fixed designs. They have been further employed in analyzing treatments for COVID-19. However, their use in critical care trials has been sparse. A profound understanding of the associated benefits of the different adaptive designs may spike their utility and interpretation. In all the methods, increasing the number of interims resulted in a reduced anticipated sample size. Under the null hypothesis, group sequential techniques offered better control of the Type I error rate. However, inflation in this error rate posed an issue for the Bayesian approaches.

This error type is commonly known as a false positive result. The false positive result causes the inaccurate rejection of the null hypothesis. It rejects the notion that must not have been rejected. Rejecting the null hypothesis by assuming that no relationship exists between the test subject, the trigger, and the result may sometimes be inaccurate. If something, except the triggers, leads to the results of a test, it may show a false positive outcome.

In hypothesis testing, it is not possible to entirely remove the possibility of this error occurring. However, there are steps that users can take to reduce the risks of getting results containing this error. One of the most common methods that helps minimize the chances of getting this error is by reducing the significance level in the hypothesis testing. The user is responsible for selecting the significance level in the hypothesis test. As a result, the user can change it. For instance, if the user reduces the significance level to 1%, the possibility of inaccurately rejecting the null hypothesis is 1%.

The differences between the two are as follows:

#1 - Type I Error

• This error occurs in hypothesis testing if the null hypothesis is true, but the user rejects it.
• They are known as false positives, as they occur when the user validates a statistically significant difference although there is none.
• These errors have the probability of “α,” which is correlated to the established level of confidence.

#2 - Type II Error

• A Type II error occurs when an individual wrongly assumes that there is no winner between a variation and a control version even though there is a clear winner.
• These errors occur if the null hypothesis is false, but the user subsequently fails to reject it.
• In this error, the probability is denoted by Beta or “β.” The Beta is based on the test’s power.

This article has been a guide to Type I Error and its definition. We explain its examples, causes, how to avoid it, consequences, and comparison with type II error. You may also find some useful articles here -

• Type II Error
• Statistical Evidence
• Null vs. Alternative Hypothesis