Preparing For The Coming Flood… Of Statistical Malfeasance | 7wData
Randy Bartlett, Ph.D. CAP® PSTAT® is a statistician/statistical data scientist with 20+ years of practice experience analyzing and reviewing data analysis; and leading business analytics teams.He designed ‘A Practitioner’s Guide to Business Analytics’ to be the foremost reference on how corporations can better implement business analytics and in this era of Big Data.
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Identifying & Understanding Statistics Problems
The nonstatistician cannot always recognize a statistical problem when he sees one.W. Edwards Deming
For business, the recent growth in fact-based decision making has provided a path to innovative new products and an escape for companies in disrupted industries. Over the coming years, we should expect a corresponding growth in statistical malfeasance. How large, you may well ask. We can not be sure, even measuring today’s statistical malfeasance is difficult. In addition to market forces, an important ingredient in this flood is a number of misunderstandings about the purview of statistics, statistical thinking, and the underlying statistical assumptions.
The key element for a successful (big) data analytics and data science future is statistical rigor and statistical thinking of humans.Diego Kuonen
The best protection from statistical malfeasance is to leverage three pillars for Best Statistical Practice: Statistical Qualifications, Diagnostics, & Review (QDR) (see ‘A Practitioner’s Guide To Business Analytics,’ McGraw-Hill (2013), Chapters 7-9). The better we understand statistics problems, the better we can identify the best Statistical Qualifications, interpret the right Statistical Diagnostics, and apply an appropriate Statistical Review. We need to use Statistical Diagnostics to measure the accuracy and reliability of results. Diagnostics are far more important for statistics problems, which do not have unique solutions in the way that we can mathematically deduce one answer. We need Statistical Review to continuously improve decision making, data analysis, and data management. Again, these three pillars are best facilitated using a savvy understanding of statistics problems.
The article continues in the May/June 2015 issue of Analytics Magazine
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