Parameter estimation is an important part of inferential statistics. It involves estimating parameters of a distribution of a population given data sampled from that population. These parameters typically represent useful numbers that help us understand that population.

To take an example, let's say that we're looking at the relationship between per-capita health care spending and life expectancy. We might determine health care costs and life expectancy for various countries, states, or other regions. Then, assuming a certain type of relationship, say linear, between these two variables, we would use parameter estimation to understand, for example, what sort of impact increasing health care spending has on life expectancy.