Maybe a bit of a philosophical question - but can you ever truly have known parameters in data? I have a set of data for which the dataset is complete, but the parameters will still be estimates i believe?
There are two circumstances where you can have "known" parameters - parameters that, when your model reports them, you know them to be true:
- Genuinely complete information, meaning you have a data set covering every single individual (or whatever) in the population, with all the possibly relevant variables known. This is, of course, a somewhat rare circumstances, but in this case you're not estimating a parameter, you're calculating it, which is a somewhat different beast philosophically. For example, if you have a desert island with 100 people on it, and you ask each one their age, the mean age you calculate is the mean, with no uncertainty.
- You're working on a simulated or otherwise constructed data set where you fixed a parameter in the data to be a particular value, and you're checking to make sure your estimator is returning the correct, known answer.
It depends what you mean by 'known' and what your data set represents.
If you mean known absolutely and with no uncertainty, then -- philosophically -- there is not much in science (hence, data science) that is 'known' in this way.
However, in every data set, there are parameters that can be assumed to be true with respect to the model underpinning the analysis that you're going to carry out.
So here what matters is what your data set represents.
If it contains physical measurements, then philosophically, every measurement is an estimate up to some degree of precision.
If it contains samples of a population, then it is an estimate of some population parameter.
In physical sciences some parameters are often known. For instance, you have fundamental constants such as the ratio of an electron charge to a mass, or the speed of sound. In social sciences there are no fundamental constants, and nothing is usually truly known, that's why there's so much advanced statistics developed in these fields to cover the lack of fundamental understanding of phenomena.
Another example, is the radioactive decay. We know that the counts are from Poisson. All you need to do is to work on the electronics of counters so it doesnt interfere too much. It's a big deal to know the distribution. You only need to estimate the rate of decay. Compare it to social sciences where at best you guess that it's maybe Poisson.
UPDATE: @whuber's comments made me reread the question, and I noticed that you mentioned
the dataset is complete
If this means that you got the population, then this changes my answer. When you have the population, there's no more estimation required. You simply compute the parameter. You estimate when there's only sample from population.