The article discusses how maximum-likelihood methods can be used to estimate parameters in scientific models. These estimators are consistent and efficient, especially when using a squared-error loss function. The sampling variance of these estimates can be calculated using methods like the bootstrap or Fisher information. The article also explains two common tests for hypotheses about these parameters: the Wald test and the likelihood-ratio test.