What is a Type II Error?

Explanation

One commonly uses type errors to create a hypothesis, determine a solution based on the probability of their occurrence, and identify the factual correction of the data that structured the hypothesis.

The diagram shows the creation of a null hypothesisNull HypothesisNull hypothesis presumes that the sampled data and the population data have no difference or in simple words, it presumes that the claim made by the person on the data or population is the absolute truth and is always right. So, even if a sample is taken from the population, the result received from the study of the sample will come the same as the assumption.read more, alternative hypothesis, sample mean, and error probability.

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Every test we undertake always has a probability of error in decision-making, and such a decision may be a Type I or Type II error. In simple words, while undertaking decision-making, we might reject the correct facts or accept the wrong facts. Rejection of correct facts is a Type I error, and accepting incorrect facts is a Type II error. This error can be very dangerous in the corporate world as the complete analysis and experiment can get wrong if the base itself is wrong.

Following is the matrix of which type of error one might undertake if facts are wrongly accepted:

A decision was taken to Reject

Example of Type II Error

  • In human beings, women tend to get pregnant. However, while verifying, a doctor mistakenly diagnoses a man to be pregnant. It is a Type II error, where the base itself was wrong.Suppose the doctor diagnoses a woman as not pregnant when she is pregnant. It is a Type I error, where the facts are correct, but the one rejects the same.

How Does Type II Error Occur?

Various factors may result in such an error.

#1 – Any change in the population is comparatively very small to detect

If the tendency to change is not visible in the population itself, then any hypothesis testingHypothesis TestingHypothesis Testing is the statistical tool that helps measure the probability of the correctness of the hypothesis result derived after performing the hypothesis on the sample data. It confirms whether the primary hypothesis results derived were correct.read more will not be able to cater to the correct facts. Such a scenario will lead to accepting incorrect facts, resulting in Type II errors.

#2 – Sample size covers a very small portion of the population

The sample selection should represent the whole population. Thus, if the sample is not an ideal population representation, it is highly unlikely to give the correct analysis. The analyst will not be able to identify the correct facts. As a result, they will rely on the wrong facts, resulting in a Type II error.

#3 – Incorrect sample selection

Generally, one uses random sampling worldwide as it is considered one of the most unbiased sample selection methods. However, many times, it results in the wrong picking of samples. As a result, it leads to incorrect population coverage and results in a Type II error.

Can Type II Errors Be Avoided?

#1 – Repeat analysis until one reaches the required significance

Significance specifies whether the null hypothesis is factually correct or not. At the end of all analyses, one accepts the null hypothesis and ensures that the facts are correct. However, often, only a single analysis cannot achieve such significance. This unilateral analysis may result in Type I or Type II errors. On the other hand, if the same kind of output comes in the repetitive analysis, one will ensure no errors occur.

#2 – In each repetition of analysis, change the size of the test of significance

As discussed in point 1, significance shows the appropriateness of the null hypothesis. If one finds that the sample is not adequately covered, one can reiterate that the size of significance increases. It will help understand the behavior and avoid a Type II error.

#3 – Alpha level around 0.1 is the ideal

Generally, an alpha of around 0.1 will result in the rejection of the hypothesis. Any rejection will allow multiple verifications. As a result, the chances of occurrence of errors reduces. Type II error occurs when anything gets wrongly accepted. If there is no scope for acceptance, such an error will not occur.

Importance

Type I Error vs Type II Error

Following are the basic difference between the two types of error:

Conclusion

Type II error is a false negative resulting from accepting an incorrect null hypothesis. In the practical world, such errors fail the full project as the base is inaccurate. Moreover, such a base may be like details, facts, or assumptions, jeopardizing the complete analysis.

This article is a guide to Type II Error and its definition. Here, we discuss examples, explanations, how it occurs, and how to avoid it. You may learn more about financing from the following articles: –

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