Question

Q24 Describe what is meant by Type I and Type II errors and explain how these can be reduced in hypothesis testing. [4 Marks]

DO NOT WRITE THE ANSWER - USE WORD FORMAT.

NO PLAGIARISM IN THE ANSWER PLEASE.

Answer 1

Type I error:

When the null hypothesis is true and you reject it, you make a type I error. The probability of making a type I error is α, which is the level of significance you set for your hypothesis test. An α of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. To lower this risk, you must use a lower value for α. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists.

Type II error:

When the null hypothesis is false and you fail to reject it, you make a type II error. The probability of making a type II error is β, which depends on the power of the test. You can decrease your risk of committing a type II error by ensuring your test has enough power. You can do this by ensuring your sample size is large enough to detect a practical difference when one truly exists.

The probability of rejecting the null hypothesis when it is false is equal to 1–β. This value is the power of the test.

	Truth about the population
Decision based on sample	H0 is true	H0 is false
Fail to reject H0	Correct Decision (probability = 1 - α)	Type II Error - fail to reject H0 when it is false (probability = β)
Reject H0	Type I Error - rejecting H0 when it is true (probability = α)	Correct Decision (probability = 1 - β)