# SPSS: 2 sample t-test: real data against fictional group with M=0 and SD=1?

We want to use SPSS to compare two groups (2 independent samples t-test). The first group contains real data, the other group is fictional. This fictional group should contain as many "subjects" as the real (1st) group. But the mean is set to 0 and the standard deviation to 1 (standard normal distribution).

There are several tools out there to compare two groups by adding the number of subjects, mean and std for every group independently. But how can we do this in SPSS? I only know how to test one variable (group1) against a test value (T=0).

EDIT: Here a more detailed description of why I wanted to use this two-sample t-test, although this kind of approach seemed strange to me in the beginning. The thing is that we investigated 20 patients and compared their data with a large database of healthy control subjects. That means we did a z-transformation using the reference database (standard procedure in this field (quantitative sensory testing,QST)).

There are other research groups which suggest to then use the 2-sample t-test I mentioned below, because it would be inappropriate to compare data of 20 patients with data of 1200 controls. So they invented a fictional group with M = 0 and SD = 1 and tested the real data against this fictional data (on a website like this --> but Two-Sample T-Test). This approach would be more conservative than using the one-sample t-test with test value=0.

To be honest, I have no idea if this is the right approach. My first idea was to do a one-sample t-test and test the patient group (i.e. their z-values) against the value 0 to see, whether the z-values differ significantly from 0 instead of using a fictional dataset.

• Katarina, what exactly would be "inappropriate" in comparing 20 patients to 1200 controls? – whuber Aug 7 '12 at 14:40
• Yes, that's the question! I don't see the point there either. And I wonder, why should I transform individual data using norm data first and then use a 2-sample t-test to test against a normal distribution? Very confusing... For those who are interested: here is the paper using and explaining that method: ncbi.nlm.nih.gov/pubmed/20965658. (But I'm not sure that you have free access to it). – Katarina Forkmann Aug 7 '12 at 15:14

• I would perhaps say compute a new variable in the current dataset using the random variable functions (it will that way be the same number of cases), then use varstocases to reshape the dataframe and then one can use the regular independent samples t-test command. I don't quite get the point of the exercise though. – Andy W Aug 7 '12 at 13:28