What do the results of a paired t-test mean?

What do the results of a paired t-test mean?

The paired sample t-test, sometimes called the dependent sample t-test, is a statistical procedure used to determine whether the mean difference between two sets of observations is zero. In a paired sample t-test, each subject or entity is measured twice, resulting in pairs of observations.

What does it mean if results are significant?

In principle, a statistically significant result (usually a difference) is a result that’s not attributed to chance. More technically, it means that if the Null Hypothesis is true (which means there really is no difference), there’s a low probability of getting a result that large or larger.

What is an example of a paired sample?

An example of paired data is blood pressure of patients before yoga (i.e., readings before the intervention) and the blood pressure of patients after yoga (i.e., readings after the intervention). The most widely used technique, testing the impact of an intervention (or for analyzing paired data), when it is numeric, is the paired sample t-test.

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 is an example of a paired t test?

The paired sample t-test is also called dependent sample t-test. It’s an univariate test that tests for a significant difference between 2 related variables. An example of this is if you where to collect the blood pressure for an individual before and after some treatment, condition, or time point.

What are paired t test assumptions?

The paired sample t-test has four main assumptions: • The dependent variable must be continuous (interval/ratio). • The observations are independent of one another. • The dependent variable should be approximately normally distributed. • The dependent variable should not contain any outliers.