No, the statement is not correct. In hypothesis testing, the 'best critical region' does not have the greatest Type I error. Instead, the 'best critical region' is the one that minimizes the probabilities of both Type I and Type II errors.

Type I error, also known as a "false positive," occurs when the null hypothesis (H0) is true, but is incorrectly rejected. The level of significance, often denoted by α, is the probability of making a Type I error.

The 'best critical region' is determined by the level of significance and the power of the test (1 - β, where β is the probability of making a Type II error). The goal is to choose a critical region that minimizes both α and β, not one that maximizes α.

So, the 'best critical region' is not the one with the greatest Type I error, but rather the one that balances the probabilities of making Type I and Type II errors.

Question

No, the statement is not correct. In hypothesis testing, the 'best critical region' does not have the greatest Type I error. Instead, the 'best critical region' is the one that minimizes the probabilities of both Type I and Type II errors.

Type I error, also known as a "false positive," occurs when the null hypothesis (H0) is true, but is incorrectly rejected. The level of significance, often denoted by α, is the probability of making a Type I error.

The 'best critical region' is determined by the level of significance and the power of the test (1 - β, where β is the probability of making a Type II error). The goal is to choose a critical region that minimizes both α and β, not one that maximizes α.

So, the 'best critical region' is not the one with the greatest Type I error, but rather the one that balances the probabilities of making Type I and Type II errors.

Knowee AI · Accepted Answer

No, the statement is not correct. In hypothesis testing, the 'best critical region' does not have the greatest Type I error. Instead, the 'best critical region' is the one that minimizes the probabilities of both Type I and Type II errors.

Type I error, also known as a "false positive," occurs when the null hypothesis (H0) is true, but is incorrectly rejected. The level of significance, often denoted by α, is the probability of making a Type I error.

The 'best critical region' is determined by the level of significance and the power of the test (1 - β, where β is the probability of making a Type II error). The goal is to choose a critical region that minimizes both α and β, not one that maximizes α.

So, the 'best critical region' is not the one with the greatest Type I error, but rather the one that balances the probabilities of making Type I and Type II errors.

In testing of hypothesis corresponding to a particular level of significance among all critical regions, ‘best critical region’ has greatest type 1 error

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In testing of hypothesis corresponding to a particular level of significance among all critical regions, ‘best critical region’ has greatest type 1 error

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