Data from real sources is rarely error free. PSPP has a number of procedures which can be used to help identify data which might be incorrect.
The DESCRIPTIVES
command (see DESCRIPTIVES) is used to generate
simple linear statistics for a dataset. It is also useful for
identifying potential problems in the data.
The example file physiology.sav contains a number of physiological
measurements of a sample of healthy adults selected at random.
However, the data entry clerk made a number of mistakes when entering
the data.
The following example illustrates the use of DESCRIPTIVES
to screen this
data and identify the erroneous values:
PSPP> get file='//share/pspp/examples/physiology.sav'. PSPP> descriptives sex, weight, height.
For this example, PSPP produces the following output:

The most interesting column in the output is the minimum value. The weight variable has a minimum value of less than zero, which is clearly erroneous. Similarly, the height variable’s minimum value seems to be very low. In fact, it is more than 5 standard deviations from the mean, and is a seemingly bizarre height for an adult person.
We can look deeper into these discrepancies by issuing an additional
EXAMINE
command:
PSPP> examine height, weight /statistics=extreme(3).
This command produces the following additional output (in part):

From this new output, you can see that the lowest value of height is
179 (which we suspect to be erroneous), but the second lowest is 1598
which
we know from DESCRIPTIVES
is within 1 standard deviation from the mean.
Similarly, the lowest value of weight is
negative, but its second lowest value is plausible.
This suggests that the two extreme values are outliers and probably
represent data entry errors.
The output also identifies the case numbers for each extreme value, so we can see that cases 30 and 38 are the ones with the erroneous values.