the classical (equally likely) definition of probability

The classical definition is the oldest and simplest recipe for a probability, and the one you met first in school: when every outcome is equally likely, the probability of an event is the number of outcomes that make it happen divided by the total number of outcomes. P(A) = (favourable outcomes) / (total outcomes). The chance of rolling an even number on a fair die is 3/6, because three of the six equally likely faces are even. No data, no philosophy — just count, then divide.

The whole method rests on one assumption that you must be entitled to make: symmetry, so that the outcomes really are equally likely. A fair coin, a balanced die, a well-shuffled deck — these earn the assumption through physical or logical symmetry, and only then is dividing counts legitimate. Where it applies, the classical definition reduces probability to careful counting, which is why combinatorics — permutations, combinations, the multiplication principle — becomes the engine room of so many probability problems. Count the favourable cases, count all cases, divide.

Its honest limits are sharp. It cannot speak to situations where outcomes are not equally likely — a bent coin, an unfair die, tomorrow's weather — and it can stumble even when symmetry seems present if you carve up the sample space into pieces that are not in fact equally likely. (Asking 'will I win the lottery: yes or no?' has two outcomes but they are nowhere near equally likely.) The classical definition is a brilliant special case, not a universal one; for the rest, you turn to the frequency or subjective interpretations.