Though chemistry, physics or microbiology, say, can tell us about some ways that
products differ, other important attributes such as pleasantness or the similarity
of one odour to another, can be measured only by using human assessors. There are well
developed methods for characterizing products in this way, but Difference Testing is a
special branch of sensory analysis, addressing a particular kind of problem.
Difference testing
Difference testing is concerned with questions such as whether or not two batches of a
product are noticeably different. Probably no two batches are
ever completely identical, given sufficiently sensitive analytical
methods, but even real differences may not be noticed by
consumers. Here are some examples.
 A company knows that the raw materials recently delivered for their
food product are in some respects different from usual.
Is the consequent change in the finished product perceptible to consumers?
 The Development Section have come up with a new recipe that has
production advantages. Do customers notice the difference?
 After how much storage time has the product changed enough to be
noticeably different?
Many types of difference test have been devised. Those in
widespread use are described in detail in textbooks of sensory
methods and in national and international standards. All take
the form of requiring someone called 'an assessor' (usually,
a panel of several assessors) to carry out a task which can be
performed perfectly by an assessor who detects the difference with
certainty but will frequently result in errors if the assessor is
unsure about the difference.
Results from difference testing are rarely clearcut. They are usually
statistical in nature, which means that answers always have some
uncertainty. The aim of statistical analysis of difference tests is
to obtain answers that are as precise as the data allow and whose
uncertainty is quantified.
One tool for quantifying the uncertainty of a difference test is a test
of statistical significance. This calculates a numerical probability,
the significance level of the result. This is the probability of
obtaining results showing at least as much detectability as was
actually observed if, in truth, the difference is completely
undetectable. This probability can be calculated, since if the
difference is totally undetectable, answers must be given at random
and the probability of being correct on any one trial can be deduced
from the nature of the test.
