Ordinal vs nominal variables Im studying data analysis and Im with a doubt between nominal and ordinal variables, because sometimes it seems difficult to understand really what kind a variable is.
For example, about nominal variables there is no meaningful rank between the categories, for example color of the eyes, or gender. And ordinal data where there is a meaningful rank between the categories, for example satisfaction with a service "Very unsatisfied,..,..,.., very satisfied".
But this is always like this? For example if I have a varialble "errortype" that can have one of this values: (200,404,403,500) this is nominal right? But its strange, because there is some kind of meaningful rank, for example code 200 is better then 404, and so on.
Other example is, If Im analysing a dataset to compare the performance of some servers and I have a variable server that can be one of this values (serverX, serverY, serverZ) and one server can have more capacity then oher, tihs is also nominal? Or because there is some kind of order, one server is better than other its ordinal?
 A: Whether a variable is treated as nominal or ordinal may depend on the
purpose for which the data were collected.
You mentioned 'eye color': On a driving license eye color is nominal;
if you're stopped for a traffic violation, that information helps the
police officer know whether the licence you show is really yours.
In a genetic study, eye color may be ordinal, with black, brown, grey/hazel,
and blue denoting a decreasing order of eye pigment that may be determined
by a particular set of genes. In the case of an ophthomologist who can scan for the
density of pigment cells in the iris, (s)he may record a count of pigment cells
per $mm^2$, and then eye color becomes a numerical variable.
Age measured in years is a numerical variable. But for reasons of privacy or vanity some people are more willing
to give an age category (20-35, 36-50, etc.) than an exact age. In that case
the categories essentially become an ordinal variable.
Your example of "error type" is puzzling. If there are three error types A, B and C,
and numbers such as 200, 403, etc. are for measuring something like cost
or delay, then you have two variables: error type is likely nominal (A, B, C),
and delay is likely numerical (not categorical).
Similarly for your example with servers: X, Y, Z may be levels of a nominal variable and there may
be another variable that measures performance. The performance variable
may be ordinal (perhaps with levels Excellent, Good, Fair, Poor) or numerical (perhaps, orders taken per hour).
In general: Political affiliation, Race, and Religion are good examples of
nominal variables. Examples of ordinal variables are ones with levels
(Strongly agree, Agree, Neutral, Disagree, Strongly Disagree), course grades
with levels (A, B, C, D, F), and so on. For ordinal variables there is a
clear ordering. 
Note: Controversy arises when people try to treat levels of an ordinal variable as
numerical. Perhaps it begins with convenience in recording: It is easier to
record 5 for 'Strongly agree', 4 for 'agree', in a spread sheet. Many statisticians agree it is seldom
valid to take numerical means of such 'numbers as labels'. It is more often to valid to speak of the 'median' category (roughly, list all SA first, A next, N next, etc.;
then pick the opinion of the middle opinion in the list).
