  Statistical systems are conceived from the standpoint of statistical
mechanics, as made of a (generally large) number of identical units and
exhibiting a (generally large) number of different configurations
(microstates), among which only equivalence classes (macrostates) are
accessible to observations. Further attention is devoted to evolving
statistical systems, and a simple case including only a possible event, E, and
related opposite event, $\neg$E, is examined in detail. In particular, the
expected evolution is determined and compared to the random evolution inferred
from a sequence of random numbers, for different sample populations. The
special case of radioactive decay is considered and results are expressed in
terms of the fractional time, $t/\Delta t$, where the time step, $\Delta t$, is
related to the decay probability, $p=p(\Delta t)$. An application is made to
data collections from selected academic courses, focusing on the extent to
which expected evolutions and model random evolutions fit to empirical random
evolutions inferred from data collections. Results could be biased by the
assumed number of students who abandoned their course, defined as suitable
impostors (SI). Extreme cases related to a lower and an upper limit of the SI
number are considered for a time step, $\Delta t=(1/12)$y, where fitting
expected evolutions relate to $0.003\le p\le0.200$. In conclusion, evolving
statistical systems made of academic courses are similar to poorly populated
samples of radioactive nuclides exhibiting equal probabilities, $p$, and time
steps, $\Delta t$, where inferred mean lifetimes, $\tau$, and half-life times,
$t_{1/2}$, range within $0.37<\tau/{\rm y}<27.73$ and $0.25<t_{1/2}/{\rm
y}<19.22$, respectively, and upper limits are related to incomplete data
collections.
