Explain type 2 error in simple terms
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Answers ( 13 )
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.
Type II Error aka False Negative Rate is nothing but Out of all the actual positives how many the model wrongly predicted them as negative.
In more simple terms referring to the above example if the model wrongly predicts married people as unmarried then it will be a Type II Error.
Type 2 errors happen when you inaccurately assume that no winner has been declared between a control version and a variation although there actually is a winner.
In more statistically accurate terms, type 2 errors happen when the null hypothesis is false and you subsequently fail to reject it.
A type II error is a statistical term referring to the acceptance (non-rejection) of a false null hypothesis.
It is used within the context of hypothesis testing. A type II error produces a false negative, also known as an error of omission.
For example, a test for a disease may report a negative result, when the patient is, in fact, infected. This is a type II error because we accept the conclusion of the test as negative even though it is incorrect.
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.
Type 2 error is basically false negative which means our model predicted false when actually itis true.
For example: If a patient has cancer but the model predicted that he/she has no cancer.
Confusion Matrix consists of True Positive, True Negative, False Positive, False Negative.
Type 2 error : when Predicting something False but actually it is True (False Negative)
As Type 1 corresponds to False Positive, Type 2 Error corresponds to False Negatives.
Not rejecting the null hypothesis, when it is actually false.
In the above picture, the woman is actually pregnant but concluded as not pregnant.
Null Hypothesis (H0): Not pregnant
Alternative Hypothesis(Ha): Pregnant
The null hypothesis is false (the woman is pregnant), but it is concluded that she is not pregnant (not rejecting the null hypothesis).
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)
Type 2 error happens when you fail to reject null hypothesis when it is wrong
It is also called false positive means in cancer prediction test predicting a person suffering from cancer as healthy
In statistical hypothesis testing, a type II error is a situation wherein a hypothesis test fails to reject the null hypothesis that is false. In other words, it causes the user to erroneously not reject the false null hypothesis because the test lacks the statistical power to detect sufficient evidence for the alternative hypothesis. The type II error is also known as a false negative.
The type II error has an inverse relationship with the power of a statistical test. This means that the higher power of a statistical test, the lower the probability of committing a type II error. The rate of a type II error (i.e., the probability of a type II error) is measured by beta (β) while the statistical power is measured by 1- β.
Example
Sam is a financial analyst. He runs a hypothesis test to discover whether there is a difference in the average price changes for large-cap and small-cap stocks.
In the test, Sam assumes as the null hypothesis that there is no difference in the average price changes between large-cap and small-cap stocks. Thus, his alternative hypothesis states that a difference between the average price changes does exist.
For the significance level, Sam chooses 5%. This means that there is a 5% probability that his test will reject the null hypothesis when it is actually true.
If Sam’s test incurs a type II error, then the results of the test will indicate that there is no difference in the average price changes between large-cap and small-cap stocks. However, in reality, a difference in the average price changes does exist.
Type 2 error is a false negative that is when something is true but you proved it wrong.