  Any physiochemical variable (Ym) is always determined from certain measured
variables {Xi}. The uncertainties {ui} of measuring {Xi} are generally a priori
ensured as acceptable. However, there is no general method for assessing
uncertainty (em) in the desired Ym, i.e. irrespective of whatever might be its
system-specific-relationship (SSR) with {Xi}, and/ or be the causes of {ui}. We
here therefore study the behaviors of different typical SSRs. The study shows
that any SSR is characterized by a set of parameters, which govern em. That is,
em is shown to represent a net SSR-driven (purely systematic) change in ui(s);
and it cannot vary for whether ui(s) be caused by either or both statistical
and systematic reasons. We thus present the general relationship of em with
ui(s), and discuss how it can be used to predict a priori the requirements for
an evaluated Ym to be representative, and hence to set the guidelines for
designing experiments and also really appropriate evaluation models. Say: Y_m=
f_m ({X_i}_(i=1)^N), then, although: e_m= g_m ({u_i}_(i=1)^N), "N" is not a key
factor in governing em. However, simply for varying "fm", the em is
demonstrated to be either equaling a ui, or >ui, or even <ui. Further, the
limiting error (d_m^(Lim.)) in determining an Ym is also shown to be decided by
"fm" (SSR). Thus, all SSRs are classified into two groups: (I) the SSRs that
can never lead "d_m^(Lim.)" to be zero; and (II) the SSRs that enable
"d_m^(Lim.)" to be zero. In fact, the theoretical-tool (SSR) is by pros and
cons no different from any discrete experimental-means of a study, and has
resemblance with chemical reactions as well.
