The variances and covariances predict a priori the best the experimental design can estimate 𝚫𝐆 𝐓𝐫𝐮𝐞 , given the model assumptions. See the Cramér-Rao bound for expanded details. We can also describe the design information by the Fisher information matrix, ℱ , which under standard assumptions is the inverse of Eq. 7 . Ultimately, with ℱ or 𝐂, we can forecast results, model precision versus cost, and design better experiments.