paired t-test and independent t-test

What are the assumptions of a Paired t-test?

• Independent data

• Skewed differences

• Median of zero for differences

• The assumptions of a Paired t-test include: 1. Paired data, 2. Normally distributed differences, 3. Mean of zero for differences, 4. Equal variances of differences.

Explain the difference between a Paired t-test and an Independent t-test.

• The Paired t-test requires equal sample sizes while the Independent t-test does not.

• The Paired t-test is more accurate than the Independent t-test.

• The Paired t-test is used for continuous data while the Independent t-test is used for categorical data.

• The main difference between a Paired t-test and an Independent t-test is the nature of the groups being compared: related vs. independent.

When should you use an Independent t-test instead of a Paired t-test?

• When the sample size is small

• When comparing means of independent groups.

• When the data is not normally distributed

• When comparing unrelated variables

Why is it important to check for normality in Independent t-test?

• To make the data more complicated

• To ensure that the data follows a normal distribution, which is an assumption of the t-test.

• To confuse the researchers

• To waste time

Can you perform a Paired t-test with non-normally distributed data? Why or why not?

• Yes, if the data is transformed to be normally distributed

• No

• Yes, because the t-test is robust to violations of normality assumptions

• Yes, as long as the sample size is large enough

What is the main difference between a one-tailed t-test and a two-tailed t-test?

• A one-tailed t-test is used for directional hypotheses while a two-tailed t-test is used for non-directional hypotheses.

• A one-tailed t-test requires larger sample sizes than a two-tailed t-test.

• A one-tailed t-test is more accurate than a two-tailed t-test.

• The main difference between a one-tailed t-test and a two-tailed t-test is the number of tails being considered: one vs. two.