The P-Value Was Never Meant To Be Used The Way It's Used Today

I have just came across Scientific method: Statistical errors  published Nature's website. I found it a very good article about the misuse and misinterpretation of p-values and about scientific evidence in general. It says, among other things, that “the P value was never meant to be used the way it's used today.”. I was not aware of the history of introduction of this measure and it was interesting thing to read. I was not surprised by some of the points about the quality of scientific research as it has been covered a lot by Taleb, Goldacre and Ioanidis to mention a few but it was nice to see the connection between these two. The author suggest an approach to curb the p-hacking effects in science but considering the nature of science studies these day I do not believe it is applicable for the majority of research. There is other good thing to learn in the article, so please read it yourself.

Ban GDP as A Driver in Regressions!

If there is one thing in analytics that grinds my gears it is the overuse of GDP as a driver in regression. If it were up me, I would ask the government and academy of science to issue a book with all approved regression drivers and distribute it as a standard repository for all knowledge and wisdom there is related to regression. Then one copy of this book and one copy only would include GDP. This copy will be in Klingon and as a precautionary measure will be booby trapped with a potent explosive and buried 100m in the ground. On the Moon. On the dark side of it.

Apply This Method For A Better Life With Spreadsheets

If you are a regular user of Excel for calculations or model building, I bet you spend quite some time to prove the calculations are correct or locating the cell formula that is messing up your impeccable design. I had had my fair share of long hours in navigating from cell to cell and sheet to sheet in search of what-is-wrong. After my frustration had reached its critical mass I realized that the quality of my spreadsheets need to be built in the development process itself. Ever since I practice this belief and hours spent in debugging has been a very small part of the whole process. I will leave aside the errors related to the calculation or model design and I would like to focus on the ones typical for excel - wrong ranges, bad formulas and all others that have the potential to ruin otherwise brilliant spreadsheet.

Friday Funny: How to Look Smart Any Place Any Time

Even a dachshund could look smart!

These days the pressure to look smart in everything, everywhere all the time is greater than ever. There are also all these moments when we have to or want to look very smart or as if we give a damn but we are bored out of our mind or the head is occupied by nothing but an enormous emptiness. Sometimes even glasses and the latest of mobile technology could not help us out. I may have a solution for this! It does not require purchase of any Apple products, clothing or facial alterations. It is a simple trick that does not requires any specific skill or knowledge.

How to Fail an Analytical Project

While there are many ways to a succeed in a project the roads to its failure seem to be very few. I would like to start a discussion of the key DON'T-s in specific area of the analytical projects. What are the things to consider before starting the project, avoid and watch for during the project? Read further for some answers.

The 10 Biggest Myths In Economics - №10 - Economics is a Science

Business Insider has a worth-reading article today - The 10 Biggest Myths in Economics. There are some good points and myths-busting. Number 10 is Economics is a Science. It goes :

"Economics is often thought of as a science when the reality is that most of economics is just politics masquerading as operational facts.  Keynesians will tell you that the government needs to spend more to generate better outcomes.  Monetarists will tell you the Fed needs to execute a more independent and laissez-fairre policy approach through its various policies.  Austrians will tell you that the government is bad and needs to be eliminated or reduced.   All of these “schools” derive many of their understandings by constructing a political perspective and then adhering a world view around these biased perspectives.  This leads to a huge amount of misconception which has led to the reason why I am even writing a post like this in the first place.  Economics is indeed the dismal science.  Dismal mainly because it’s dominated by policy analysts who are pitching political views as operational realities."

Predictive Analytics and Prescriptive Analytics

Few days ago I read a post by Javier Lafuente Garcia clarifying the differences between predictive and prescriptive analytics. The author lays clear and concise definitions as well as points some applications of each type. I like the way he does that and I would like to share it.

Developing Big Data Analytics Strategy

The torrent of big data and analytics articles rarely touches on how to get to the point where all the nice charts and discoveries magically appear out of the analysts hands (keyboards actually). Getting an organization to the point of producing meaningful results requires a mountain of focused efforts. I have recently came across of an article that concisely and comprehensively covers the steps leading making (big) data analytics possible and meaningful.

Better Human Understanding, Not Big Data, Is the Future of Business

Recently I came across a nice article that articulates some of the issues with the data analytics in organizations and too much relying on it. In the height of the big data hype it is a sane reminder of the limitations of data as a business driver. No need retelling you the story - read it yourself here.

Markets' Natural Levels And Natural Drivers

Recent hype of all sorts of data analysis, big data and expanding application of complex math make more and more media and people refer to macroeconomic indicators when explaining what is going on. This is OK for overviews and inconsequential analysis to fill up air time and gap between drinks. When it comes to analysis that would have impact on business and government decision these factors have to be  accompanied with understanding of real drivers behind processes. Rarely I hear this type though. We should not forget that real-world processes are driven by real people and companies making decision based on their incentives, beliefs, legislation, etc. Maybe it is the scientist in me speaking but we should always search for the reasons  for one thing or another to happen. That leads to the idea of the "natural level" of each market or any other field of interest.