Which of the following is the null hypothesis for a two-sample t-test?

Which of the following is the null hypothesis for a two-sample t-test?

The default null hypothesis for a 2-sample t-test is that the two groups are equal. You can see in the equation that when the two groups are equal, the difference (and the entire ratio) also equals zero.

How do you interpret the p-value for a two-sample t test?

Usually, a significance level (denoted as α or alpha) of 0.05 works well. A significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. If the p-value is less than or equal to the significance level, the decision is to reject the null hypothesis.

When should we use a paired t test?

Paired t-tests are used when the same item or group is tested twice, which is known as a repeated measures t-test. Some examples of instances for which a paired t-test is appropriate include: The before and after effect of a pharmaceutical treatment on the same group of people.

When is it appropriate to use the paired difference t test?

The paired t test is generally used when measurements are taken from the same subject before and after some manipulation such as injection of a drug. For example, you can use a paired t test to determine the significance of a difference in blood pressure before and after administration of an experimental pressor substance.

When to use a paired t test?

The paired t-test is used when the variable is numerical in nature (for example, the height of a person or the weight of a person) and the individuals in the sample are either paired up in some way (such as a husband and wife) or the same people are used twice (for example, preprocedure and postprocedure).

What are t-tests for independent and paired samples?

Two-sample t-tests for a difference in mean involve independent samples (unpaired samples) or paired samples. Paired t-tests are a form of blocking, and have greater power than unpaired tests when the paired units are similar with respect to “noise factors” that are independent of membership in the two groups being compared.