Year: 2016

1. Government spends a lot of money tackling the toughest least profitable problems
2. Govt has trouble recruiting the best hackers , computer scientists and statisticians (data science community) as they generally get a lot more salary in private sector for far more easy problems ( which ad do I want them to click)
3. Private companies in USA can also outsource or get H1 visa workers for analytical needs while even USA government has to rely on US citizen data scientists for small non-sensitive departments like calculating subsidies for factory farms for Department of Agriculture.
4. Meanwhile the budget for IT digitization for electronic government and Data Science is quite small
5. Govt has lot more bureaucracy and lack of speed to get things done which is a big turn off for companies trying to be new data science vendors thus leaving a big hand to pricey players like AH BEE HUMMhttp://www.youtube.com/watch?v=25QyCxVkXwQ
6. http://www.youtube.com/watch?v=25QyCxVkXwQ
7. In election season, data scientists are in even more shortage as they work for analyzing and calculating odds for winning states or even work in teams for candidates ( political parties pay everyone working for them by cheque in US )
8. Information security is one more area where they lack enough recruitment strategy
9. Hackers have a general aversion to working for any Government ( for less salary) unless they are endowed with equity ( here successes companies like INQTEL (https://www.iqt.org/) can be replicated not just for Intelligence but for other departments as well by startup funds in hacker
10. Software interfaces need to be updated for better data visualization and analytical communication across departments
11. More money can be invested in training existing Federal Employees in analytics, analytical way of thinking or even basics of data science

One more note- US government can repair its relationship by the hacker activist community even by small courtesies and track 2 diplomacy. That can help not just with business as usual data science (like where is rain going to fall in Florida and Lousiana for Department of Agriculture) but also special areas of mutual concern (identifying hateful events through crowd sourced intelligence across public social media data

P Values have now become controversial. P here does not stand for President Trump but this.

https://matloff.wordpress.com/2016/03/07/after-150-years-the-asa-says-no-to-p-values/

Sadly, the concept of p-values and significance testing forms the very core of statistics. A number of us have been pointing out for decades that p-values are at best underinformative and often misleading. Almost all statisticians agree on this, yet they all continue to use it and, worse, teach it. I recall a few years ago, when Frank Harrell and I suggested that R place less emphasis on p-values in its output, there was solid pushback. One can’t blame the pusherbackers, though, as the use of p-values is so completely entrenched that R would not be serving its users well with such a radical move.

Click to access P-ValueStatement.pdf

The American Statistical Association (ASA) has released a “Statement on Statistical Significance and P-Values” with six principles underlying the proper use and interpretation of the p-value [http://amstat.tandfonline.com/doi/abs/10.1080/00031305.2016.1154108#.Vt2XIOaE2MN]. The ASA releases this guidance on p-values to improve the conduct and interpretation of quantitative science and inform the growing emphasis on reproducibility of science research. The statement also notes that the increased quantification of scientific research and a proliferation of large, complex data sets has expanded the scope for statistics and the importance of appropriately chosen techniques, properly conducted analyses, and correct interpretation.

I personally think Big Data needs Bigger Thinking among statisticians about a new era of inference.

However as always these guys are the best