How quickly does a model's accuracy and precision improve with increasing sample size? Indeed, there are counter examples (like the periodogram estimator for ps

How quickly does a model's accuracy and precision improve with increasing sample size? Indeed, there are counter examples (like the periodogram estimator for psd) where variance does not decrease with window length. We often don't know which model to use. Occam's Razor [1] can be effective in favouring simpler models, but I tend toward the view that a good data scientist is invariably needed to build good models. Hence I view Big Data more as a consulting business than SaaS. [1] For an excellent Bayesian discussion on why Occam's Razor actually works, see Chapter 28 of David J.C. MacKay's book 'Information Theory, Inference and Learning Algorithms'.