Hypothesis Testing for proportions

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What is the null hypothesis in hypothesis testing?

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The null hypothesis (H0) is a statement that there is no effect or no difference, and it serves as the default assumption that any observed effect is due to sampling variability.

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

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The significance level (α) is the probability of rejecting the null hypothesis when it is actually true. It is commonly set at 0.05 or 0.01.

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How do you calculate the p-value in hypothesis testing?

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The p-value is calculated based on the test statistic and the sampling distribution under the null hypothesis. It represents the probability of observing data as extreme as, or more extreme than, the observed data.

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What does it mean to reject the null hypothesis?

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Rejecting the null hypothesis means that there is sufficient evidence to support the alternative hypothesis, indicating that the observed effect is statistically significant.

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What is the alternative hypothesis (H1)?

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The alternative hypothesis (H1) is a statement that indicates the presence of an effect or a difference. It is what researchers aim to support through their testing.

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What is a Type I error in hypothesis testing?

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A Type I error occurs when the null hypothesis is rejected when it is actually true. This is also known as a false positive.

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What is a Type II error in hypothesis testing?

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A Type II error occurs when the null hypothesis is not rejected when it is actually false. This is also known as a false negative.

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What is the formula for the test statistic in a proportion hypothesis test?

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What is the standard error (SE) for a proportion?

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What does a p-value of 0.2212 indicate in hypothesis testing?

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A p-value of 0.2212 indicates that there is a 22.12% probability of observing the data, or something more extreme, if the null hypothesis is true. This is typically not considered statistically significant at common α levels.