Understanding, Calculating, and Using Statistical Power in Sample Size Determinations for Significance Tests

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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:

  • Vocabulary and Concepts
  • t-Tests and p-values
  • Statistical power
  • for t-Tests
  • critical difference to detect
  • example calculations
  • Power Curves
  • Software programs

Who Will Benefit:

  • QA/QC Supervisors
  • Process Engineers
  • Manufacturing Engineers
  • QC/QC Technicians
  • Manufacturing Technicians
  • R&D Engineers
  • Design/manufacturing in the Medical Device and Pharmaceutical Industries

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