The maximum likelihood estimator (MLE) is used in statistics. What does it aim to estimate?

The maximum likelihood estimator (MLE) is a method in statistics used to estimate the parameters of a probability distribution. It operates under the principle of finding the parameter values that maximize the likelihood function, which represents how likely a particular set of observations is, given a statistical model. By focusing on the parameters of the distribution, MLE provides a way to infer the underlying structure that generated the data.

For example, if you have a set of observations from a normal distribution, MLE would help you determine the best estimates for the mean and variance of that distribution by maximizing the likelihood of observing the collected data under different parameter settings. This makes it particularly powerful for various types of distributions and is commonly used across different statistical applications.

In contrast, while the other options pertain to specific characteristics of datasets or populations, they do not encompass the broader scope of MLE's capability to address parameter estimation comprehensively. Thus, the focus on the general estimation of distribution parameters distinguishes the correct answer from the others.