P-Values in Hypothesis Testing

What is the null hypothesis when comparing two sample means?

• The two sample means are equal.

• The two sample means are not equal.

• The two sample means are greater than each other.

• The two sample means are less than each other.

Which of the following is the formula for calculating the test statistic for a one-sample mean test?

What is the p-value?

• The probability of observing a test statistic as extreme as, or more extreme than, the observed value under the null hypothesis.

• The probability of the null hypothesis being true.

• The probability of the alternative hypothesis being true.

• The probability of observing the sample mean.

In hypothesis testing, what does a p-value less than 0.05 typically indicate?

• Strong evidence against the null hypothesis.

• Strong evidence for the null hypothesis.

• No evidence against the null hypothesis.

• No evidence for the null hypothesis.

Which of the following is the formula for calculating the test statistic for a two-sample mean test assuming equal variances?

What is the null hypothesis when comparing two sample proportions?

• The two sample proportions are equal.

• The two sample proportions are not equal.

• The two sample proportions are greater than each other.

• The two sample proportions are less than each other.

Which of the following is the formula for calculating the test statistic for a one-sample proportion test?

Which of the following is the formula for calculating the test statistic for a two-sample proportion test?

What does a p-value greater than 0.05 typically suggest in hypothesis testing?

• Insufficient evidence to reject the null hypothesis.

• Sufficient evidence to reject the null hypothesis.

• The null hypothesis is true.

• The alternative hypothesis is true.

In the context of hypothesis testing, what is a Type I error?

• Rejecting the null hypothesis when it is true.

• Failing to reject the null hypothesis when it is false.

• Accepting the null hypothesis when it is false.

• Accepting the alternative hypothesis when it is true.