This talk is about several details that arise in teaching. Firstly, for parameter theta with estimator theta-hat, the same symbol theta-hat commonly denotes the estimate, which is a realisation of the estimator. Inspired by David Chew, theta^* is proposed instead, and the utility in understanding Monte Carlo bootstrap and confidence intervals will be demonstrated. Secondly, it will be shown that realisations offer a complete and accurate interpretation of the Strong Law of Large Numbers, which is accessible to undergraduate students. Finally, weak convergence results can be strengthened in most cases of MOM and ML estimators, to the extent that the Weak Law of Large Numbers need not feature in an introductory course on inference.