MAR 28, 2019 9:00 AM PDT

Analytical Variations: Old Problem - New Consequences

C.E. Credits: P.A.C.E. CE Florida CE
  • CEO, Certus Analytics, Founder, CannaSafe Analytics
      Matt Haskin was the founder and previous CEO of CannaSafe Analytics, which, under his vision and leadership, became the first cannabis laboratory in the United States to be awarded an ISO 17025:2005 accreditation in 2012. He holds a Bachelor of Science degree from Columbia State University and has extensive experience in both domestic and international business. A chronic entrepreneur, Matt struck upon the concept of a medical cannabis laboratory in late 2009 after observing close family and friends struggle with concerns over the safety of the cannabis they used as medicine. With no industry established protocols or methods for testing cannabis, Matt set out to develop a lab that would set the standard and earn the trust of its clients. In February 2015 Matt was appointed Vice Chair of the Independent Laboratory Advisory Committee to the state of Nevada. The Committee makes policy recommendations on the state's cannabis program. Matt is also a member of the AOCS Cannabis Expert Panel, sits on the board of directors of the California Minority Alliance and is a member of the Green Standard Working Group. He was honored to be nominated and evaluated for the position of Bureau Chief of the California Bureau of Cannabis Control. Matt sold CannaSafe in October 2016 and now heads up Certus Analytics, a BCC licensed California cannabis compliance laboratory.


    Analytical variation, most commonly referred to as - varying lab results - can be both inter-lab as well as intra-lab. Lab results permeate virtually all segments of the industry, from B2B purchasing decisions to consumer confidence and brand building. It is not new, nor is it unique to cannabis, but that doesn’t make it any less frustrating or downright maddening to the business owners that are subjected to it. And to compound matters, heading into the second quarter of 2019, the economic consequences can be severe. 

    Learning Objectives: 

    1. We will learn about the Uncertainty of Measurement, the role it plays in analytical variation and how to minimize uncertainty. 
    2. Understanding how to improve the quality of the measurement. 

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