Language Skills: Statistical Abstraction

Does how we talk impact what we say?

In a recent DM Review article, the authors discuss different ways of accounting for two types of error based on using a sample: how tight the estimates are around a given value (precision) and how far the average estimate is from the truth (bias). Got to give them credit for avoiding terms like "stochastic" in the discussion.

However, what's missing in this discussion is why this matters from a business point of view. Or in the words of another economist - what is the 'oomph' of the analysis? It would have been helpful to understand scenarios where precision is more important than bias and vice versa. For instance what class of problems should focus on consistency, even if biased, versus overall accuracy?

This article left me thinking that it's no wonder firms like Williams Sonoma, Capital One, and Cisco are currently looking for senior people to manage customer information and analytical teams while simultaneously talking with C-level executives.