Hi

This blog came into being as I found myself making more and more observations on the role managerial statistics could play in our work. And as I have lately been getting deeper into quantitative analysis at work, there have been quite a few very interesting things I noticed. I will try to share them with you, mainly so that I can get critical feedback on my methods and observations. Of course, for NDA reasons I will not be taking names of my company or its clients here. Neither shall any "data" be extracted or derived from my work.

Okay, now that we are done with the disclaiming bit, here are a few things to watch out for.

1. Take the context and source of data into careful consideration. I cannot over-emphasize this. It has been stated over and over in all the literature I have read that data taken out of context is just numbers, and drawing conclusions as such is suicide.

2. Do not assume applications of a given technique. In all probability it was designed for a specific set of conditions and you should factor in all the differences between the case you are reading and your own situation. A very common mistake of this sort: assuming that correlation determines causality.

3. Mathematical techniques must be followed to the T. It is common to see enthusiasts consider only the stated output of the calculation and plug in whatever comes in handy. Common mistakes of this sort are using non-normalized data and using unadjusted data (where normalization or adjustment are clearly called for). How does one know? Read, read, read. Find a crisp article that clearly states the usage of the particular calulation.

All said and done, I hope you find this blog easy to read and helpful, and that you will comment freely. The effort here is to make and realize mistakes in theory, so that the practice is perfect.