In this scenario, a correct decision has been made. The hypothesis test concluded that the population mean is more than 215, and in reality, the population mean is indeed more than 215. Therefore, there is no error in this case.

In hypothesis testing, there are two types of errors that can occur:

1. Type I error: This occurs when we reject the null hypothesis when it is actually true. In other words, we conclude that there is an effect when there isn't one.

2. Type II error: This occurs when we fail to reject the null hypothesis when it is actually false. In other words, we conclude that there is no effect when there actually is one.

In this case, neither of these errors has occurred, so a correct decision has been made.

Question

In this scenario, a correct decision has been made. The hypothesis test concluded that the population mean is more than 215, and in reality, the population mean is indeed more than 215. Therefore, there is no error in this case.

In hypothesis testing, there are two types of errors that can occur:

1. Type I error: This occurs when we reject the null hypothesis when it is actually true. In other words, we conclude that there is an effect when there isn't one.

2. Type II error: This occurs when we fail to reject the null hypothesis when it is actually false. In other words, we conclude that there is no effect when there actually is one.

In this case, neither of these errors has occurred, so a correct decision has been made.

Knowee AI · Accepted Answer

In this scenario, a correct decision has been made. The hypothesis test concluded that the population mean is more than 215, and in reality, the population mean is indeed more than 215. Therefore, there is no error in this case.

In hypothesis testing, there are two types of errors that can occur:

1. Type I error: This occurs when we reject the null hypothesis when it is actually true. In other words, we conclude that there is an effect when there isn't one.

2. Type II error: This occurs when we fail to reject the null hypothesis when it is actually false. In other words, we conclude that there is no effect when there actually is one.

In this case, neither of these errors has occurred, so a correct decision has been made.

A hypothesis test is completed and we have sufficient evidence that a certain population mean is more than 215. Suppose that in reality the population mean is more than 215. Determine if a correct decision has been made. If an error has been made, indicate which type of error it is.

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