1342 - Final Review Questions - Test #4

Front

What is the null hypothesis (H0) in hypothesis testing?

Back

The null hypothesis (H0) is a statement that there is no effect or no difference, and it serves as the default assumption that is tested against an alternative hypothesis.

Front

What does it mean to 'Fail to Reject H0'?

Back

Failing to reject H0 means that there is not enough evidence to support the alternative hypothesis (Ha), and the null hypothesis remains a plausible explanation.

Front

What is an alternative hypothesis (Ha)?

Back

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

Front

What is a right-tailed hypothesis test?

Back

A right-tailed hypothesis test is used to determine if a parameter is greater than a certain value, focusing on the right tail of the distribution.

Front

What is a left-tailed hypothesis test?

Back

A left-tailed hypothesis test is used to determine if a parameter is less than a certain value, focusing on the left tail of the distribution.

Front

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

Back

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

Front

How do you find the critical value for a hypothesis test?

Back

The critical value is determined based on the significance level (α) and the sample size (n), using statistical tables or software.

Front

What does it mean if the p-value is less than α?

Back

If the p-value is less than α, it indicates strong evidence against the null hypothesis, leading to its rejection.

Front

What is the role of sample size (n) in hypothesis testing?

Back

Sample size (n) affects the power of the test and the precision of the estimate; larger samples generally provide more reliable results.

Front

What is the difference between Type I and Type II errors?

Back

Type I error occurs when the null hypothesis is rejected when it is true (false positive), while Type II error occurs when the null hypothesis is not rejected when it is false (false negative).