May 2021 Spray 29
Statistical calculations on the three data sets in Figure 1 indicate
that there is an approximately 80% chance that package
#2 is more corrosion-resistant than package #1. The same
calculations indicate that packages #1 and #3 are statistically
the same, plus packages #2 and #3 are also statistically the
same. Statistical calculations are not commutative; therefore
packages #1 and #2 are not statistically the same even though
packages #2 and #3 are the same.
Hence, package #2 is indeed the most corrosion-resistant
package of those tested; as well, corrosion test results are the
same for packages #1 and #3 even though their means are
different. Conclusions based only on statistical means are
often incomplete and could lead to unexpected corrosion in
commercial aerosol products.
Is it an outlier or an extreme value?
Sometimes one or more replicate samples in a group of
nominally identical samples have results that are significantly
higher or lower than the majority of the group. Are the small
groups of samples extreme values or outliers? Outliers can
be omitted from the analysis, but extreme values must be
included.
Extreme values are only outliers when there is an assignable
reason, such as the package was filled incorrectly—what
I refer to as an “experimental-oops.” Extreme values are
outliers when there is an experimental-oops associated with
the sample.
For example, the product in a single spray package is a
water-out emulsion, but the product in all the other packages
are oil-out emulsions (as specified). The corrosion data
for the package with the unexpected water-out emulsion is
an outlier and can be excluded from the corrosion analysis.
However, it should be determined why the emulsion for the
single sample was different from the rest of the group; just in
case there is a systemic problem with emulsion stability that
might cause package corrosion.
Extreme value corrosion data provide very valuable
information about failures (e.g., leaking or non-spraying
containers). For example, an individual extreme value sample
having a very low service lifetime indicates that a portion of
the packages filled during a year might also have low service
lifetimes.
In this situation, the percentage of extremes in the data is
used to estimate the risk of corrosion failures (leaking or not
spraying) for the entire population. In other words, extreme
values also provide a means to estimate risk and prevent
unexpected package failures.
In summary, avoid exclusively using statistical means as
the only decision-making test parameter. Instead, consider
using statistical inference tests, such as the student t-test and/
or the Box & Whisker plot along with the means. These
others statistical analyses include extremes and provide more
accurate measures for whether or not data from supposedly
different test variables are indeed different. In addition, an
extreme value is only an outlier if there is an experimentaloops
associated with the extreme.
Thanks for your interest and I’ll see you in June. Please contact
me at 608-831-2076, rustdr@pairodocspro.com or from one of
our two websites pairodocspro.com and aristartec.com. Spray