Getting Back to the Basics
It has oft been said that Data Science consists of three pillars. Statistics, Computer Science and Domain Expertise. In literature and in the media it seems that basic statistical theory gets ignored. I guess it is due to the fact that statistics is not always intuitive and as a long standing pillar of mathematics lacks the hype and sexiness for lack of a better word that big data, machine learning and artificial intelligence have.
I must admit that I am partially guilty of that too but recently I came across a really good article that highlights the power of understanding and being able to apply basic statistics. How not to analyze noisy data: A case study The R function used in the article is explained here: Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors
For a quick review of Standard Errors, try this: Khan Academy on Standard Error