Final answer:
Hypothesis testing is a statistical method used to make inferences about a population based on a sample of data. It involves formulating a null hypothesis and an alternative hypothesis, and using statistical tests to determine whether there is enough evidence to reject the null hypothesis.
Step-by-step explanation:
Hypothesis testing is a statistical method used to make inferences about a population based on a sample of data. It involves formulating a null hypothesis and an alternative hypothesis, collecting data, and using statistical tests to determine whether there is enough evidence to reject the null hypothesis. The Type I error occurs when the null hypothesis is true, but is rejected, while the Type II error occurs when the null hypothesis is false, but is not rejected.
For example, suppose you are testing a new drug to see if it is effective in treating a certain disease. The null hypothesis would be that the drug has no effect, while the alternative hypothesis would be that the drug does have an effect. You collect data from a sample of patients and analyze it using statistical tests. If you reject the null hypothesis and conclude that the drug is effective, but in reality it doesn't have any effect, then it is a Type I error. On the other hand, if you fail to reject the null hypothesis and conclude that the drug is not effective, but in reality it does have an effect, then it is a Type II error.