I think it depends on what sort of question you're asking, but for this post consider that you're interested in how the frequency of claims has changed since last year.

One approach would be to look at all the date, which includes every single claim, and say "Yes, there are more claims per person now than last year," and there's no error in that estimate at all, because you have measured the true population parameter.

But you might imagine that the event that any one person files a claim is a random variable, say with a bernoulli distribution, so P(claim)=p and p(no claim)= 1-p. If you also assume that this distribution holds for everyone (a big assumption, but it's just an example), then the total number of claims, X, from all n people has a Binomial distribution w/ parameters n and p. You have X from this year and X from last year, and you can ask, has p changed from last year to this year? Here's where statistical inference will come in--even if X is higher this year, it may not be larger enough to suggest a significantly different change in p. (flip a coin 100 times and then 100 more. If you get more heads the second time, do you assume the coin has become biased towards heads?) You're still doing inferential stats, even though you have all available data, because your parameter of interest is never directly observed.

Does this make sense?