Module 10 Practice Questions (3)

Front

What is the null hypothesis (Ho) in hypothesis testing?

Back

The null hypothesis (Ho) 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.

Front

What is the alternative hypothesis (Ha) in hypothesis testing?

Back

The alternative hypothesis (Ha) is a statement that indicates the presence of an effect or a difference, suggesting that the observed data is not due to chance.

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What does a p-value represent in hypothesis testing?

Back

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

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What does it mean if a p-value is less than the significance level (e.g., 0.05)?

Back

If a p-value is less than the significance level, it indicates that there is significant evidence to reject the null hypothesis in favor of the alternative hypothesis.

Front

What is a Type I error in hypothesis testing?

Back

A Type I error occurs when the null hypothesis is incorrectly rejected when it is actually true.

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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.

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What is a confidence interval?

Back

A confidence interval is a range of values derived from a sample that is likely to contain the population parameter with a specified level of confidence (e.g., 95%).

Front

What does it mean if a sample mean falls within a confidence interval?

Back

If a sample mean falls within a confidence interval, it suggests that there is not enough evidence to conclude that the population mean is different from the sample mean.

Front

What is the significance of a sample size in hypothesis testing?

Back

A larger sample size generally provides more reliable estimates of population parameters and increases the power of a hypothesis test.

Front

What is the role of standard deviation in hypothesis testing?

Back

Standard deviation measures the variability or dispersion of a set of data points, which is crucial for calculating confidence intervals and conducting hypothesis tests.