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).

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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
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