**Univariate case**

In **paired sample** hypothesis testing, a sample from the population is chosen and two measurements for each element in the sample are taken. Each set of measurements is considered a sample, but the samples are not independent of one another. Paired samples are also called **matched samples** or **repeated measures**. Examples include:

- Comparing the driving skills of people before and after they take a driver’s training course
- Determining whether the attitudes of husbands and wives differ regarding capital punishment
- Determining the effectiveness of a mosquito repellent by applying the repellent to the right arm of each subject in the sample (but not the left arm) and determining whether the right arms have fewer bites than the left arms.

Here we have two samples for the random variables *x* and y and test the null hypothesis that the population mean of *x* and y are equal. This is equivalent to the one-sample t-test on the random variable *z = x – y*. Specifically, we test the null hypothesis H_{0}: *μ*_{z}* = *0, which is equivalent to H_{0}: *μ _{x}* =

*μ*

_{y}.

**Multivariate case**

The multivariate case is very similar to the univariate case. Now we have two samples for the random vectors *X* and *Y* and test the null hypothesis that the population mean vectors of *X* and Y are equal. This is equivalent to the One Sample Hotelling’s T^{2} Test on the random vector *Z = X – Y*. Specifically, we test the null hypothesis H_{0}: *μ*_{Z}* = *0, which is equivalent to H_{0}: *μ _{X}* =

*μ*. We illustrate the approach used in the following example.

_{Y}**Example 1**: The shoe company from Example 1 of One Sample Hotelling’s T^{2} Statistic is considering phasing out an existing shoe model (Model 2) by the prototype described in Example 1 of One Sample Hotelling’s T^{2} Statistic. The company had the same subjects evaluate both Model 1 and Model 2, and looked to see if there was a significant difference between the two models which would help them decide whether to replace Model 2 by Model 1. The sample is shown in Figure 1.

**Figure 1 – Data for paired samples Hotelling’s T ^{2} model**

Essentially we perform the same analysis as in One Sample Hotelling’s T^{2} Statistic, with the goal being 0 for the difference between the two evaluations for each criteria. We repeat the analysis in the next few figures.

**Figure 2 – Goal for difference between the two models**

**Figure 3 – Output shows significant difference between the two models**

From Figure 3, we see there is a significant difference between the two shoe models. We next determine for which criteria there is a significant difference, using both 95% simultaneous confidence intervals and 95% Bonferroni confidence intervals.

**Figure 4 – 95% simultaneous confidence intervals**

**Figure 5 – 95% Bonferroni confidence intervals**

Hi,

I am trying to compare two matrices of same size and find out if they are similar or not by using a statistical test.Can I use this test for comparison or are there any better tests?

Hi TJ,

You can use the Paired Sample T-square test to compare matrices, but many other tests may also be suitable or better. It all depends upon what sort of data is contained in the matrices and what you are trying to test.

Charle

Sir,

I have percentage distribution of two variables in matrix form. Now, I have to compare two matrices through some test and check if there is any significant difference between the two matrices.

TJ,

If you are checking whether there is a significant difference between the two distributions you can use the two sample Kolmogorov-Smirnov test. If the data are normally distributed you could use the two sample t test to determine whether the means are significantly different. There are other tests, but this is a start. You can click on the highlighted links to get more information.

Charles

Sir,

Do you have any literature on the paired sample Hotelling’s T-square test.I really appreciate any help you can provide.

TJ,

You can take a look at the references in the website Bibliography. I believe reference PS2 could be especially helpful.

Charles

Sir,

Is there any limit for the t2 value. In my case, it is going up to 400.

TJ,

I don’t know of any limits on the values of t2. I have run a model where t2 is more than 12,000.

Charles

Sir,

Is it possible to to use the two sample Kolmogorov-Smirnov test to compare the joint distribution of two variables obtained in matrix form?

TJ,

There are multivariate forms of the two sample Kolmogorov-Smirnov test, but I have not used them. I found the following paper which may be useful to you: http://www.uam.es/personal_pdi/ciencias/ajustel/papers/1997-spl.pdf

Charles

Hi Sir,

Can I ask for the steps done after the hotellings t square? The post hoc doesn’t give a very detailed explanation. Thanks in advance!

Dave,

Since for the Hotelling’s T-square tests there are only two

independentvariables (Model 1 and Model 2), if there is a significant difference, a post-hoc analysis looks at whichdependentvariables are making a significant contribution to the difference (if any). The referenced webpage only lightly touches on this, but the One sample Hotelling’s T-square test webpage goes into a lot more detail (the section calledConfidence Intervals). But since the Paired Samples test is simply a One Sample test on the differences, the analysis is the same.sir please is it possible to use t squared to test the relationship between mortality rates and incidence rate? or maybe fertility rate and mortality rates? thank you

Jimmy,

If you are just comparing mortality rates and incidence rates, then a simple paired t test may be sufficient. If there are multiple dependent variables then the paired t-squared test could be appropriate (if the assumptions for the test are met).

You need to provide additional information before I can give a more definitive answer.

Charles

On-Market Day Off-Market Day

food items market A market B market A market B

x1 y1 x2 y2 x1 y1 x2 y2

1

2

3

.

.

.

26

pls what statistical tool will be more appropriate?

x1 means price during Xmas, X2 means Price after Xmas, y1 and y2 means distcance of food item from source.

thank you

On-Market Day Off-Market Day

food items market A market B market A market B

x1 y1 x2 y2 x1 y1 x2 y2

Gabby,

Sorry, but I don’t understand your example well enough to reply.

Charles

im adam from ghana msc applied stats i known have better undestanding of hos test tanks sir

Dear Charles

Does the number of subjects in each group has to be the same for a Paired Sample Hotelling’s T-square test or can this test still be performed when there are different numbers of participants?

Thanks

Sarah,

The sample consists of pairs, and so the number of subjects in each has to be the same.

Charles

Is it appropriate to enter only the variables that are significant into the Hotelling test, i.e. first perform separate paired t-tests, then discard the ones that are not significant, and enter the remaining significant outcomes into the Hotelling test? For example, say there are 10 dependent variables, we first carry out ten t-tests — 5 came out significant (i.e., p<.05). Take these 5 dependent variables and enter them into the Hotelling test (discard the other 5 that were not significant). Then use the results of the Hotelling test to rank these 5 dependent variables — from most significant to least significant, i.e. rank the variables in order of their significance using the Hotelling test. There may be other more straightfoward methods, e.g. effect size, etc. I would appreciate your thoughts on using the Hotelling test to rank significant dependent variables.

Raymond,

I wouldn’t do that for the following reasons: (1) increase in experimentwise error and (2) this ignores the impact of the correlations among the dependent variables.

Charles

Yes, the five dependent variables in the Hotelling test (in my example) should not be significantly correlated. Let’s assume they are not … and I also understand the part about the inflated alpha, let’s “ignore” this for the time being, if you don’t mind.

Raymond,

If the dependent variables are not significantly correlated there is nothing to be gained by using Hotelling’s test. You can simply conduct paired t tests.

Charles