Parametric vs. Non-parametric tests, and when to use them

The fundamentals of Data Science include computer science, statistics and math. It's very easy to get caught up in the latest and greatest, most powerful algorithms -- convolutional neural nets, reinforcement learning etc. As an ML/health researcher and algorithm developer, I often employ these techniques. However, something I have seen rife in the data science community after having trained 10 years as an electrical engineer is that if all you have is a hammer, everything looks like a nail. Suffice it to say that while many of these exciting algorithms have immense applicability, too often the statistical underpinnings of the data science community are overlooked.