AP Statistics Type I and Type II Errors

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What is a Type I Error in statistics?

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A Type I Error occurs when a true null hypothesis is rejected. It is also known as a 'false positive.'

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What is a Type II Error in statistics?

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A Type II Error occurs when a false null hypothesis is not rejected. It is also known as a 'false negative.'

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What is the significance level (alpha) in hypothesis testing?

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The significance level (alpha) is the probability of making a Type I Error. It is commonly set at 0.05.

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What is the power of a statistical test?

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The power of a statistical test is the probability of correctly rejecting a false null hypothesis, which is 1 minus the probability of a Type II Error.

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How do Type I and Type II errors relate to hypothesis testing?

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Type I and Type II errors are two potential errors that can occur in hypothesis testing, affecting the validity of the test results.

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What is a null hypothesis (H0)?

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A null hypothesis (H0) is a statement that there is no effect or no difference, and it is the hypothesis that researchers aim to test.

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What is an alternative hypothesis (H1)?

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An alternative hypothesis (H1) is a statement that indicates the presence of an effect or a difference, opposing the null hypothesis.

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What is a p-value in hypothesis testing?

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A p-value is the probability of obtaining test results at least as extreme as the observed results, assuming that the null hypothesis is true.

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What does it mean to reject the null hypothesis?

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Rejecting the null hypothesis means that the evidence suggests that the null hypothesis is unlikely to be true, leading to support for the alternative hypothesis.

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What does it mean to fail to reject the null hypothesis?

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Failing to reject the null hypothesis means that there is not enough evidence to support the alternative hypothesis, but it does not prove that the null hypothesis is true.