Understanding Hypothesis Testing

What are the null and alternative hypotheses in a hypothesis test?

• The null hypothesis (H0) is always true; the alternative hypothesis (H1) is always false.

• The null hypothesis (H0) is a guess; the alternative hypothesis (H1) is a proven fact.

• The null hypothesis (H0) states no effect or difference; the alternative hypothesis (H1) states there is an effect or difference.

• The null hypothesis (H0) states there is an effect; the alternative hypothesis (H1) states no effect.

How do you determine the rejection region for a one-tailed test?

• The rejection region is found by calculating the p-value.

• The rejection region is determined by the sample size only.

• The rejection region is determined by the critical value corresponding to the significance level in the specified tail of the distribution.

• The rejection region is based on the sample mean alone.

What is the significance level in hypothesis testing?

• The significance level in hypothesis testing is the probability of rejecting the null hypothesis when it is true.

• The significance level is the threshold for determining the sample size in an experiment.

• The significance level indicates the strength of the alternative hypothesis.

• The significance level is the probability of accepting the null hypothesis when it is false.

How do you calculate the p-value for a given test statistic?

• The p-value is calculated by averaging the test statistic values.

• The p-value is determined by the sample size alone.

• The p-value is the difference between the observed and expected values.

• The p-value is calculated by finding the probability of observing a test statistic as extreme as the one obtained, under the null hypothesis.

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

• It suggests the data is not reliable.

• It indicates no significant difference exists.

• It means to accept the null hypothesis.

• It means to reject the null hypothesis.

In a two-tailed test, how do you find the critical values?

• Calculate critical values using only one tail of the distribution.

• Use statistical tables to find critical values at alpha/2 for both tails.

• Determine critical values by multiplying alpha by the sample size.

• Use the mean of the sample data to find critical values.

What is the relationship between the p-value and the rejection region?

• The p-value determines whether a test statistic falls within the rejection region.

• The p-value indicates the exact value of the test statistic.

• The p-value is unrelated to the rejection region.

• The rejection region is determined by the sample size only.

How do you state the null hypothesis for a test of means?

• H0: μ1 > μ2

• H0: μ1 < μ2

• H0: μ1 ≠ μ2

• H0: μ1 = μ2

What is the effect of increasing the sample size on the p-value?

• Increasing the sample size makes the p-value irrelevant.

• A larger sample size always increases the p-value.

• Increasing the sample size has no effect on the p-value.

• Increasing the sample size can lead to a smaller p-value.

How do you interpret a p-value of 0.03 in the context of hypothesis testing?

• The p-value indicates a strong correlation between variables.

• Accept the null hypothesis; results are statistically insignificant.

• Fail to reject the null hypothesis; results are inconclusive.

• Reject the null hypothesis; results are statistically significant.