# Type 1 vs Type 2 Error: Difference and Comparison

When a researcher rejects a null hypothesis that is actually true and accepts a null hypothesis that is actually false, Type 1 and Type 2 mistakes occur.

There are four situations that are likely to arise during the acceptance or rejection of a null hypothesis. Among these four possible situations, two are correct. The other two lead to incorrect results and are known as errors in statistics.

## Key Takeaways

1. Type 1 error occurs when a true null hypothesis is rejected, leading to a false positive result.
2. Type 2 error arises when a false null hypothesis is not rejected, causing a false negative outcome.
3. Researchers aim to minimize errors by adjusting significance levels, sample sizes, and study designs.

## Type 1 Error vs Type 2 Error

The difference between type 1 and type 2 errors is that type 1 mistake happens when a researcher rejects the null hypothesis when it is true actuality. In contrast to that, type 2 error occurs when a researcher takes the wrong decision of accepting a null hypothesis because it is wrong in reality. The rate of error that can take place in type 1 is denoted by alpha. The rate of error that can take place in type 2 is denoted by beta.

Rejection of reality and acceptance of false reality by a researcher is a type 1 error. One common reason for making type 1 errors is improper research and sample size. It is also called the error of the first kind.

Acceptation of false reality and rejection of reality by a researcher is a type 2 error. This error is likely to occur when the sample size is not determined appropriately. The rate of this error is denoted by beta (a Greek letter).

## What is Type 1 Error?

A null hypothesis is rejected by a researcher in a type 1 error, yet it is true in fact. Research involving a certain population is done so as to find out whether a null hypothesis is true or false.

Many times this research involving a certain test can be interpreted wrongly, and this is when errors occur.

One of these types of errors is called the type 1 error. In type 1 error, the null hypothesis is actually true in reality, but the researcher tends to reject it.

This error is referred to as alpha error as the probability of occurrence of this error is denoted or represented by a Greek symbol alpha.

So, if the researcher takes a correct decision regarding the null hypothesis after its testing, then its probability comes to 1 minus alpha.

In simple words, it can be said as the probability of non-occurrence of type 1 error is 1 minus the probability of its occurrence (alpha).

Let’s take an example of a type 1 error; a student does not go to the canteen because he thinks it is closed. He ends up on this decision after some research from his friends, but in reality, the canteen is open.

In this situation, the boy is making a decision to reject the null hypothesis, which is actually true in reality. In terms of statistics, this is recognized as a type 1 error.

## What is Type 2 Error?

In a type 2 mistake, a researcher commits the mistake of accepting a null hypothesis. In this scenario, the researcher accepts the null hypothesis once the investigation is completed, although it is untrue in reality.

The probability of occurrence of this error is considered to be represented by the Greek symbol beta. Hence, this error is also called a beta error.

The probability of not committing this error (type 2 error) is 1 minus the probability of occurrence (beta). This one minus beta is the case when the researcher is taking the correct decision, which is the rejection of the hypothesis.

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It is addressed as a power of a test. It can be said as the probability of not committing a type 2 error.

In order to lower the type 2 test occurrence, one should increase the power of a test. This can be done conveniently by increasing the sample size.

Let’s take an example of a type 2 error; a student does go to the canteen because he thinks it is open. He ends up on this decision after some research from his friends, but in reality, the canteen is closed.

In this situation, the boy is making a decision to accept the null hypothesis, which is actually false in reality. In terms of statistics, this is addressed as a type 2 error.

## Main Differences Between Type 1 and Type 2 Error

1. There is a rejection of reality by the researcher in the type one error, whereas the researcher accepts the false reality in the type two error.
2. In type 1 error, the null hypothesis, in reality, is true, whereas in type 2 error, the null hypothesis, in reality, is false.
3. The probability of type 1 error taking place is alpha, whereas that of type 2 error is beta.
4. Many refer to type 1 error as an error of the first kind and type 2 error as an error of the second kind.
5. Type 2 error can be reduced to a certain extent by decreasing the level of alpha, whereas type 2 error can be reduced by increasing the alpha level.
References

Last Updated : 09 August, 2023

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### 21 thoughts on “Type 1 vs Type 2 Error: Difference and Comparison”

1. It was interesting to read about type 1 and type 2 errors, and the importance of minimizing them to avoid incorrect results in research.

2. This is very important information for researchers. Being aware of the elements that contribute to Type 1 and Type 2 errors can improve the accuracy of their studies.

• Agreed. It’s essential to understand the potential errors in statistical analysis and take steps to minimize them.

3. The article provides a comprehensive overview of Type 1 and Type 2 errors, emphasizing the critical role of researchers in minimizing these errors to ensure the reliability of their findings.

• Absolutely, researchers need to be mindful of these potential pitfalls and strive to minimize errors through rigorous study designs and statistical analyses.

• The insights provided in this article are highly informative, offering valuable guidance for researchers aiming to improve the accuracy of their research.

4. The discussion of how to reduce Type 1 and Type 2 errors through adjustments to alpha and beta is enlightening for researchers aiming to improve the accuracy of their work.

• Good point. It’s crucial for researchers to be mindful of these probabilities when interpreting their findings.

5. The explanations of Type 1 and Type 2 errors provide a comprehensive understanding of the potential mistakes in research, emphasizing the need for vigilance in minimizing these errors.

• Absolutely, understanding these errors is essential for researchers to ensure the reliability and validity of their studies.

• The discussion of how to reduce errors by adjusting significance levels and sample sizes provides practical guidance for researchers to enhance the accuracy of their work.

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7. Understanding the difference between these two types of errors and the factors influencing them is crucial for conducting reliable research.

• Absolutely, researchers need to be diligent in minimizing these errors by adjusting significance levels, sample sizes, and study designs.

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8. The real-world examples provided for Type 1 and Type 2 errors make the concept more relatable and easier to understand in practical terms.

• I agree, the examples help illustrate how these errors can impact research outcomes and the importance of avoiding them.

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