Calculation of power is so complicated that it typically must be done with a software program. Even so, the software program's output can be misunderstood unless the user has a firm understanding of the basic concept of statistical power. This seminar helps the attendee to understand and use the output of power calculations, to determine adequate sample sizes and to determine whether or not a product or process meets requirements.
Why Should You Attend:
Whenever a test of statistical significance is conducted with the hope that the result will be non-significant, the results may be unacceptable to a regulatory agency unless the test had an acceptable level of "power". FDA typically requires a minimum of 80% power, and often requires 90% power.
This seminar explains power basics, by using a t-test as an example. One of the very many possible formulas is then demonstrated, as well as 2 different software programs and their "Power Curves".
Statistical power is an indicator of the ability of a test of significance to "detect" a practical difference (e.g., between the averages of two products that are being compared). A low power typically means that the sample sizes in the study are too small. Without an analysis of statistical power, a conclusion of "non-significant" is rightfully questionable. Unless power is high, a study may be doomed to failure even before it is begun.
This webinar provides thorough training in how to interpret and use the power-analysis outputted by text-book calculations or software programs modules.
Areas Covered in the Webinar:
Who Will Benefit:
Physical CD-DVD of recorded session will be despatched after 72 hrs on completion of payment
Recorded video session
John Zorich has spent almost 40 years in the medical device manufacturing industry; the first 20 years were as a "regular" employee in the areas of R&D, Manufacturing, QA/QC, and Regulatory; the next 15 years were as a consultant in the areas of QA/QC and Statistics. These last few years were as a trainer and consultant in the area of Applied Statistics only. His consulting clients in the area of statistics have included numerous start-ups as well as large corporations such as Boston Scientific, Novellus, and Siemens Medical. His experience as an instructor in applied statistics includes having given annual 3-day seminars for many years at Ohlone College (San Jose CA), and previously having given that same course for several years for Silicon Valley ASQ Biomedical. He's given numerous statistical seminars at ASQ meetings and conferences. And he creates and sells validated statistical software programs that have been purchased by more than 110 companies, world-wide.