binbagsss said:
Q1) I don't understand why ##W## is large is needed for ##p(n)W## to describe the number of systems that sit in state ##|n>##? Why doesn't this hold for small ##n##?
In my opinion, statistical physics uses very cumbersome language because it wishes to use
actual frequencies of events instead of dealing with
probabilities of events. If we take a small number of systems (e.g. 3 systems ) and there are a large number possible states (e.g. 1000) , then even in the case where all the states have approximately the same
probability of being occupied, only at most 3 of them would
actually be occupied. So the observed frequency ratios would show many instances of 0/1000 instead of all of them being 1/1000. However, if take a large number of systems then the actual frequency ratios would
probably all be close to 1/1000. (Physics wants to proceed on the assumption that the actual frequency ratios
are definitely close to 1/1000. So, using the language of ensembles, we speak words that conclude by making this assumption.)
Q2)Also probably a stupid quesiton, but in what ways to the states ##|n>##, which physical properties are allowed to differ, since isn't the idea to take a large number ##W## of identical copies of the system ##S##, or do they not neeed to be identical?
If we take the viewpoint of probability theory, we postulate there is a population of things, each of which is in one and only one of a set of states ##\mu_1, \mu_2,...\mu_N##. We assume there exist a set of variables ##P_1, P_2,..P_n## such that each member of the population is associated with one and only set of values of those variables. (i.e. a given member of the population "has" specific numerical values of ##P_1,P_2,...P_n##.
Given a specific numerical values of ##P_1,P_2,...P_n##, define the subpopulation ##W## to be the subset of the population such that each member of it associated with those specific numerical values. Define ##p(\mu_i)## to be the probability that a randomly selected member of ##W## is in state ##\mu_i##.
The members of ##W## are "identical" with respect to the numerical values of ##P_1,P_2,..P_n## that they have, but not identical with respect to which state ##\mu_i## that they are in.
That pattern could be applied to almost any situation, so it's interesting to ask why only particular instances of it are useful. For example we could define the "state" of a person to be his yearly income in dollars. We could define a set of numerical properties associated with a person such as weight, height, length of forearm, etc.Associated with each specific set of numerical values of the properties, there is a probability distribution for a randomly selected person with those numerical values being in the various states. However, the utility of this viewpoint is dubious because it's unlikely that there are any simplifying formulas that give ##p(\mu_i)## as a function of the numerical values of those properties. Finding ##p(\mu_i)## for a given set of numerical values (e.g. 180 lbs, 6 ft, 23. 9 inches) can be done by sampling or tabulating data from the whole population, but we don't have a simple formula that let's us plug-in the numerical values of the properties and compute ##p(\mu_i)##. By contrast, for gases in equilibrium, the properties of temperature, pressure, etc. for gases in equilibrium
are related to the ##p(\mu_i)## by mathematical formulae.