ttest
for Dependent Samples  Arrangement of Data
Technically, we can apply the ttest for dependent samples to
any two variables in our data set and the selection of variables is identical
to that used for Correlations. However, applying
this test will make very little sense if the values of the two variables
in the data set are not logically and methodologically comparable. For
example, if you compare the average WCC
in a sample of patients before and after a treatment but use a different
counting method or different units in the second measurement, then a highly
significant ttest value could
be obtained due to an artifact; that is, to the change of units of measurement.
Following, is an example of a data set (spreadsheet) that can be analyzed
using the ttest for dependent
samples.

WCC
before 
WCC
after 
case
1 
111.9 
113 
case
2 
109 
110 
case
3 
143 
144 
case
4 
101 
102 
case
5 
80 
80.9 
... 
... 
... 

average change
between WCC
"before"
and "after" = 1 
The average difference between the two conditions is relatively small
(d=1) as compared to the differentiation (range) of the raw scores (from
80 to 143, in the first sample). However, the ttest
for dependent samples analysis is performed only on the paired differences,
"ignoring" the raw scores and their potential differentiation.
Thus, the size of this particular difference of 1 will be compared not
to the differentiation of raw scores but to the differentiation of the
individual difference scores, which is relatively small: 0.2 (from 0.9
to 1.1). Compared to that variability, the difference of 1 is extremely
large and can yield a highly significant t value.