As we discussed in an earlier news article , focusing solely on p-values when performing statistical analysis can be misleading and doesn’t make best use of all the information in the data. These concepts have been further reinforced in a recent special issue in The American Statistician journal titled Statistical Inference in the 21st Century: A World Beyond p < 0.05 . Their recommendations were summarized by the editors as follows, “ We summarize our recommendations in two sentences totaling seven words: A ccept uncertainty. Be T houghtful, O pen, and M odest. Remember ATOM . ” Much of what’s proposed by the editors is fundamental to the practice of risk analysis, where the explicit modeling of uncertainty and the use of more diverse and flexible statistical distributions have been advocated for decades, so we certainly agree with their recommendations.  

 

With the availability of modern methods such as Bayesian statistics and Monte Carlo simulation , we have much more flexibility to produce results that are relevant to our audience. In our case, being a consultancy, we are typically asked to use modeling to support decisions that affect an institution’s goals, so rather than using p-values we talk about confidence in the superiority or optimality of different actionable options, rankings between them, and the magnitude and impact of those options. A hypothetical example based on our practice could inform if option A is better than option B to reach a company’s certain growth target based on an analysis of the means using historical data. Using a one-sided test (A>B) one could only say, “we reject the null hypothesis that A=B, (P<.05)”, and perhaps state that option A yielded an average $5M more revenues than option B. At this point we would be escorted out of the client’s offices as this information would be largely useless to the decision maker. If we instead use Bayesian methods, an alternative answer could be “based on historical information we are 99% confident that your revenues will increase by at least $5M if you stay with option A…but there is also a 25% chance that future revenues would be higher if you implement option B”. With this information together with the costs of each option and the risk appetite of the institution, we can incorporate a decision-analytical framework to help the institution decide what’s the optimal option. Furthermore, one can then use simulation methods to create different scenarios going forward and see how the conclusions from historical data could be affected by variables we might encounter in the future. We invite you to review a repository of freely available applied example models illustrating these principles.

EpiX Analytics had the pleasure of running a customized 10-day training workshop in Quantitative Risk Assessment in Food Safety for the risk assessment team at the Food Standards Agency in London, UK.

Taught by Dr. Francisco Zagmutt , the workshop provided participants with strong foundations in quantitative risk assessment (QRA), and introduced the latest methods in microbiological and chemical food safety QRA. During the workshop, participants worked on a variety of applied exercises and also developed quantitative risk assessment projects of their choice under the guidance of the instructor. We believe that this hands-on learning approach is key to building long-term QRA skills.

If you are interested in learning such QRA skills, there are still a few spots left in our upcoming class in Fort Collins, May 20-24, 2019. See the Course Schedule below.

Dr. Kurt Rinehart, senior consultant at EpiX Analytics, was a presenter at the Society of Decision Professionals' annual DAAG 2019 Data + Decisions conference held in Denver, CO, March 5-8.

As a participant on the Energy Panel: Leveraging data to make great decisions, Kurt spoke about some of the great opportunities and challenges of enhancing the decision making process with analyses and models based on empirical data within the oil & gas industry.

The SDP's DAAG conference provided the opportunity for members of the EpiX team to participate in workshops related to such topics as decision analysis in various fields, data science, strategy development, and project management.

During the spring semester, EpiX will be delivering guest lectures in the "Decision Modeling" course at the University of Colorado's Leeds School of Business. EpiX will also deliver a guest lecture in the "Financial Risk Management" course at Colorado State University's College of Business.

These lectures focus on the practical aspects and value of applying decision and risk modeling in real-world situations across a range of industries.