Quality Progress - May 2017 - 31

How can we prove the performance of a newly designed
product is equivalent to the performance of a standard,
already-available product or a new-performance target? How
do we verify that the life of a newly designed, lightweight and
slim smartphone battery, for example, is equivalent to the life of
the heavier battery currently on the market?
Traditional hypothesis testing looks for differences, but if we
need to prove equivalence, we need a different type of test.
Equivalence testing is a method sometimes used in industries
regulated by the U.S. Food and Drug Administration, but it can
apply to any new product design. It is done during design verification and completed prior to the design being transferred to
manufacturing.
Equivalence testing is reviewed by specialists and cross-functional teams within the design and final development stage to
confirm the design or to detect and correct any deficiencies early
on. In executing equivalence testing in design verification, manufacturers establish techniques based on a valid statistical rationale.

Traditional hypothesis testing

In the verification phase, manufacturers must demonstrate the
performance of a product is good enough to meet its target with
a specific confidence level while staying within its specification.
It therefore is assumed that the requirements have not been met
unless testing demonstrates otherwise.
The common statistical test used to demonstrate product performance is the null hypothesis significance test (NHST). NHST
is a method used to test the quality level
that would be accepted (such as 95% of
Just the
the time) by estimating a performance of
Facts
the product population through a sample.
The NHST is set up like this:
Design verifiH 0: µ ND − µSD = 0 vs. HA: µ ND − µSD ≠ 0
The null hypothesis, H 0 , means the new
design is good. The alternative hypothesis, HA , means the new design is rejected.
µ ND is the mean of product performance
collected from the newly designed
products-the mean daily lives of a new
lightweight smartphone battery, for example. µSD is a mean of product performance
collected from standard or targeted available products in the market.
Under traditional hypothesis testing, we
assume the null hypothesis, so we assume
a statement such as, "There are no mean

cation using
hypothesis testing
looks for differences between
two designs.
Just because
a hypothesis
test shows two
designs aren't
different does not
mean they are the
same.

Equivalence
testing using a
two one-sided
test ensures a new
design is the same
or better than one
currently on the
market.

qualityprogress.com ❘ May 2017

QP 31


http://www.qualityprogress.com

Table of Contents for the Digital Edition of Quality Progress - May 2017

Seen and Heard
Expert Answers
Progress Report
Mr. Pareto Head
Field Notes
Mind the Gaps
Solid Footing
Are These the Same?
New Tricks for an Old Tool
Innovation Imperative
Experience More
Career Coach
Statistics Spotlight
Standard Issues
Marketplace
Footnotes
Back to Basics
Quality Progress - May 2017 - intro
Quality Progress - May 2017 - cover1
Quality Progress - May 2017 - cover2
Quality Progress - May 2017 - 1
Quality Progress - May 2017 - 2
Quality Progress - May 2017 - 3
Quality Progress - May 2017 - 4
Quality Progress - May 2017 - 5
Quality Progress - May 2017 - Seen and Heard
Quality Progress - May 2017 - Expert Answers
Quality Progress - May 2017 - Progress Report
Quality Progress - May 2017 - Mr. Pareto Head
Quality Progress - May 2017 - 10
Quality Progress - May 2017 - 11
Quality Progress - May 2017 - Field Notes
Quality Progress - May 2017 - 13
Quality Progress - May 2017 - 14
Quality Progress - May 2017 - 15
Quality Progress - May 2017 - Mind the Gaps
Quality Progress - May 2017 - 17
Quality Progress - May 2017 - 18
Quality Progress - May 2017 - 19
Quality Progress - May 2017 - 20
Quality Progress - May 2017 - 21
Quality Progress - May 2017 - 22
Quality Progress - May 2017 - 23
Quality Progress - May 2017 - Solid Footing
Quality Progress - May 2017 - 25
Quality Progress - May 2017 - 26
Quality Progress - May 2017 - 27
Quality Progress - May 2017 - 28
Quality Progress - May 2017 - 29
Quality Progress - May 2017 - Are These the Same?
Quality Progress - May 2017 - 31
Quality Progress - May 2017 - 32
Quality Progress - May 2017 - 33
Quality Progress - May 2017 - 34
Quality Progress - May 2017 - 35
Quality Progress - May 2017 - New Tricks for an Old Tool
Quality Progress - May 2017 - 37
Quality Progress - May 2017 - 38
Quality Progress - May 2017 - 39
Quality Progress - May 2017 - 40
Quality Progress - May 2017 - 41
Quality Progress - May 2017 - 42
Quality Progress - May 2017 - 43
Quality Progress - May 2017 - Innovation Imperative
Quality Progress - May 2017 - 45
Quality Progress - May 2017 - 46
Quality Progress - May 2017 - Career Coach
Quality Progress - May 2017 - 48
Quality Progress - May 2017 - 49
Quality Progress - May 2017 - Statistics Spotlight
Quality Progress - May 2017 - 51
Quality Progress - May 2017 - 52
Quality Progress - May 2017 - 53
Quality Progress - May 2017 - Standard Issues
Quality Progress - May 2017 - 55
Quality Progress - May 2017 - 56
Quality Progress - May 2017 - 57
Quality Progress - May 2017 - Marketplace
Quality Progress - May 2017 - 59
Quality Progress - May 2017 - Footnotes
Quality Progress - May 2017 - 61
Quality Progress - May 2017 - 62
Quality Progress - May 2017 - 63
Quality Progress - May 2017 - Back to Basics
Quality Progress - May 2017 - cover3
Quality Progress - May 2017 - cover4
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