Statistical Power – Understanding, Calculating, and How to Effectively Use it

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 (e.g., StatgraphicsCenturionXV).

Why You Should 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. 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 webinar explains the 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”.

Areas Covered in the Session :

• Vocabulary and Concepts
• t-Tests and p-values
• Statistical Power
  • For t-Tests
  • Critical Difference to Detect
  • Example Calculations
  • Power Curves

Who Should Attend:

• QA/QC Supervisors
• Process Engineers
• Manufacturing Engineer
• QC/QC Technicians
• Manufacturing Technicians
• R&D Engineers

Course Director: JOHN N. ZORICH

John Zorich has spent 35 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 last 15 years were as consultant in the areas of QA/QC and Statistics. 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 statistics includes having given 3-day workshop/seminars for the past several years at Ohlone College (San Jose CA), 1-day training workshops in SPC for Silicon Valley Polytechnic Institute (San Jose CA) for several years, several 3-day courses for ASQ Biomedical, numerous seminars at ASQ meetings and conferences, and half-day seminars for numerous private clients. He creates and sells formally-validated statistical application spreadsheets that have been purchased by more than 75 companies, world-wide