## Explain type 2 error in simple terms

Type 2 error in simple terms

Confusion Matrix is one of those concepts which is a bit confusing but is also asked to candidates in order to check their clarity of concepts.

There could be questions like,”Explain a model which is 99% accurate but still of no use to the company”

The answer lies to the understanding of the concepts of confusion matrix

Type 2 error in simple terms

You will encounter this error while solving a Classification problem.

You will always produce a confusion matrix while solving a classification problem,

irrespective of the algorithm you use.

A confusion matrix is a 2*2 matrix consisting of True Positives, True Negatives, False Positives, False Negatives.

Type 2 Error corresponds to False Negatives.

To say in layman terms, predicting something False when it is actually True.

For Example – Predicting a person will not default on the Loan when in reality has defaulted on the Loan.

The above has been contributed by the user **spawlaw007**

Username – **smk** has defined the above question in the following way

1) A statistically significant result cannot prove that a research hypothesis is correct (as this implies 100% certainty). Because a p-value is based on probabilities, there is always a chance of making an incorrect conclusion regarding accepting or rejecting the null hypothesis (H0)

2) Anytime we make a decision using statistics there are four possible outcomes, with two representing correct decisions and two representing errors

TYPE II ERROR:

1) A type II error is also known as a false negative and occurs when a researcher fails to reject a null hypothesis which is really false. Here a researcher concludes there is not a significant effect when actually there really is

2) You can decrease the risk of type II error by having a large sample size

TYPE I ERROR:

1)A type 1 error is also known as a false positive and occurs when a researcher incorrectly rejects a true null hypothesis

2) This means that your report that your findings are significant when in fact they have occurred by chance

3) The probability of making a type I error is represented by your alpha level (α), which is the p-value below which you reject the null hypothesis EXAMPLE: A p-value of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis.

You can reduce your risk of committing a type I error by using a lower value for p. For example, a p-value of 0.01 would mean there is a 1% chance of committing a Type I error. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists (thus risking a type II error)

There are 15+ users who have answered this questions, you can explore all the answers to have a clear understanding of Type-2 error

Link to question – http://thedatamonk.com/question/explain-type-2-error-in-simple-terms/

We have covered 40+ complete Data Science company interviews from the candidates who cracked these interviews.

Data Science Companies interview questions

We also have 30+ e-books on Amazon, Insta Mojo and books which can be delivered directly on your email address

Complete Set of e-books from The Data Monk

Understand some of the very complex topics in Analytics which are asked in most of the interviews

The Data Monk Top Articles

How to become a Data Scientist? Complete study material, free resources and websites to practice

Become a Data Scientist

Make your profile on our website and practice at least 5-7 questions per day. Be a part of ~2000 Analytics expert

Keep Learning 🙂

Nitin Kamal

Co-Founder | The Data Monk

## Leave a reply