recipes : Statistics : Performing a t-test


How do I perform a t-test in MATLAB?


The MATLAB Statistics Toolbox contains the ttest command, which performs one-sample and paired tests. There is also the ttest2 command for performing an un-paired t-test.

Defining some terms
First of all, let's define some terms. A one-sample t-test asks whether the mean of a distribution is significantly different from a particular value (often zero). A two-sample t-test asks whether the means of two different distributions are the same. A paired test indicates that the data from the two groups are linked in some way. For example, you've measured the weight of 10 different peacocks on day A and again on day B, and you want to know if their weights have altered. Since you have measured each peacock twice, you want to do a paired test. An un-paired test is one where the data from the two groups are not linked. For example, you've measured 10 peacocks from Italy and 10 peacocks from Mongolia and you want to know if their average weights differ. Since you have measured different specimens in each group, you perform an unpaired test. Choosing the right test is important, as we will see below. The a Wikipedia page discusses the assumptions of the test. I'm not going into them here, beyond saying that the data need to be fairly normally distributed and ideally have have fairly similar variance (although there are versions of the t-test which waive the equal variance assumption) . At larger sample sizes, the normality assumption becomes increasingly less important.

Let's warm up by running a one-sample t-test.

data=randn(1,24)+0.5; %Generate and plot some random data. 
one sample box plot

Now we test if the mean is significantly different from zero.


h =

p =

ci =
    0.0667    0.8852

stats = 
    tstat: 2.4060
       df: 23
       sd: 0.9691

Understanding the outputs
The ttest command returns a bunch of stuff in those 4 output arguments. If you look at the help page you will get an overview of what it all is. Let's quickly go over the output for our test.

Whilst that's a lot to take in, the outputs of the test are the same for the other scenarios (e.g. two-sample, un-paired, etc). We will now look at two further things: testing different null hypotheses and why paired tests matter.

Choosing a suitable null hypothesis
There is no law in statistics that says the null hypothesis must be a "nil hypothesis", or zero effect. Let's take the one-sample case above, in that case the null hypothesis was that the mean of the distribution was zero. But it need not have been. We could have tested whether the mean was 0.5, or 1.0, or any other number. First I'll tell you why this matters, then I'll show you how to do it.

Let's take a toy example: say that you work in a lab which studies the forearm strength of the platypus by asking them to push on a lever. You have a great idea to see if platypus forearm strength is improved by homeopathy. You know that homeopathy is not taken seriously by most people so you decide to include lots of statistical tests to make your study look really scientific. You measure the ability of your platypuses to push a bar before they been homeopathised (i.e. this is the control condition), and find that they exert an average force of 15 N. You do a t-test (as above) and show that this is "significant" because it's significantly different from zero. So what? It's a straw-man test: if the platypuses are able to perform the lever-pushing task they will exert some force on the bar. This test will always be significant. So testing a nil hypothesis isn't interesting. Instead, you should test for something meaningful. For example, why not ask whether forearm strength at the start of the study (before the homeopathy) is similar to that found by previous work? That will tell the reader that your platypuses were normal and healthy. You look in the literature and see that platypus forearm strength is on average 15.9 N. Let's test if this is significantly different from the 15 N you measured:

%Make 8 random platypuses with a mean forearm strength of 15N 

%Is that different from 15.9 N?

h =

p =

Nope. It turns out that your sample of 8 animals have a mean strength that's not significantly different from that found previously. That's good! It means you can go ahead with the next phase of your study: testing the effect of homeopathy.

Why paired tests matter
The platypus example is working for me, so let's carry on with that. The 8 animals were tested before being given the homeopathy treatment and again afterward. You want to know if there was an effect. First you make a pair of box plots with the raw data overlaid, then you do a t-test.

hold on
p(2)=plot(zeros(size(after))+2, after, 'o');
hold off
paired plot
>> [h,p]=ttest2(before,after)           

h =

p =

Oh no! There's no effect of the homeopathy on forearm strength. Now what? Following a sleepless night, you return to the lab and realise that you did an un-paired test. In other words, a test that ignores the fact that you tested the same platypuses in the two conditions. Let's do it right. The first thing to realise is that you can make a better plot. You should link the raw data points in the two graphs:


hold on
for ii=1:length(before)
hold off
paired plot

Hmm... Look! In all cases but one the forearm strength has gone down. Let's see what a paired t-test tells us:


h =

p =

Ah. Now the difference exceeds the conventional (but arbitrary) 5% significance threshold, indicating that the decrease in forearm strength was significant. The conclusion (based upon this small sample size) is that forearm strength goes down following homeopathic treatment. You decide to repeat the study at a higher drug dilution to see what happens. At this point we leave you to your own devices...


It's easy to do t-tests in MATLAB and there are various options (not covered here) for tweaking the way the tests are done. See the MATLAB help pages (linked to above) for more details. Although the above is a silly example, it does hit a lot of the key points. Remember that producing the most informative plot is always a good way of guiding your analysis to the most appropriate test. Also remember that the results of the stats test should echo what you're seeing in your plot. If the two don't seems to match up, something is wrong.


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