# How does an unpaired t-test work?

## How does an unpaired t-test work?

The unpaired t test works by comparing the difference between means with the standard error of the difference, computed by combining the standard errors of the two groups. If the data are paired or matched, then you should choose a paired t test instead.

What is the difference between paired and unpaired data?

There are two types: paired and unpaired. Paired means that both samples consist of the same test subjects. A paired t-test is equivalent to a one-sample t-test. On the other hand, unpaired means that both samples consist of distinct test subjects.

### What is an unpaired t-test example?

The unpaired two-samples t-test is used to compare the mean of two independent groups. For example, suppose that we have measured the weight of 100 individuals: 50 women (group A) and 50 men (group B). Therefore, it’s possible to use an independent t-test to evaluate whether the means are different.

What does paired t-test mean?

A paired t-test is used when we are interested in the difference between two variables for the same subject. Often the two variables are separated by time. Since we are ultimately concerned with the difference between two measures in one sample, the paired t-test reduces to the one sample t-test.

## How do I know if my data is paired or not?

Two data sets are “paired” when the following one-to-one relationship exists between values in the two data sets.

• Each data set has the same number of data points.
• Each data point in one data set is related to one, and only one, data point in the other data set.

How do you know if a paired t test is significant?

If the p-value is less than or equal to the significance level, the decision is to reject the null hypothesis. You can conclude that the difference between the population means is statistically significant. Use your specialized knowledge to determine whether the difference is practically significant.

### What are two assumptions of a t-test?

The common assumptions made when doing a t-test include those regarding the scale of measurement, random sampling, normality of data distribution, adequacy of sample size, and equality of variance in standard deviation.