In a laboratory study of two different industrial cleansers CleanAll was found to remove 40 more dirt and kill 30 more bacteria than the next best cleanser Furthermore a study showed that employees working at buildings cleaned with CleanAll used far fewer

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In a laboratory study of two different industrial cleansers, CleanAll was found to remove 40% more dirt
and kill 30% more bacteria than the next best cleanser. Furthermore, a study showed that employees
working at buildings cleaned with CleanAll used far fewer sick days than employees working in buildings
cleaned with other cleansers. Therefore, to prevent employee illness, all companies should use CleanAll as
their industrial cleanser.

In this argument, the author suggests that all companies should adopt CleanAll as their industrial
cleanser. This is based on several facts that the author cites. The argument depends, however, on certain
additional assumptions which are not explicitly stated and which, should they prove unfounded, may
critically weaken the final conclusion.
For example, the author references the fact that CleanAll performed better than the next best
cleanser in a laboratory study of two industrial cleansers. Before this piece of evidence can be used to
support the argument that CleanAll should be adopted by all companies, several things need to be
established. First of all, it is important to know the origin of this study. Is the organization that performed
this study reliable? The argument assumes that it is, but if it is not this would greatly weaken its thrust.
Additionally, and importantly, we are never told what this “next best cleanser” is. Suppose that CleanAll
was the second-worst industrial cleanser. If it were compared with the worst cleanser, it may very well
boast the same facts that it removes more dirt and bacteria. In order for this claim to have weight, then, it is
crucial to know the scope of the comparison. As is, the assumption that this comparison cleanser is highquality
is unsupported.
Another assumption on which the argument depends is the notion that sick days are a valid measure
of the effectiveness of an industrial cleaner. The study demonstrates that the employees of companies
using CleanAll take far fewer sick days. But we must not jump immediately to the conclusion that this
differences was caused by the effectiveness of CleanAll. Were the two companies randomly selected?
Were other possible variables controlled for? And finally, does fewer sick days mean a better cleaner? As
is, the argument assumes that each of these is the case—for if any of these weren’t the case, it would be
unwarranted to jump to the final conclusion that all companies should adopt the cleaner.
The final assumption the argument makes is that a sweeping claim can be made from limited
evidence. Suppose that all the assumptions stated above were indeed correct—that CleanAll performed
better than leading cleansers, not just low-quality ones, and that its use in a company does indeed cause
employees to take fewer sick days. Even if these were all the case, it wouldn’t necessarily indicate that all
companies should adopt the product. Perhaps there are other considerations that influence companies’
choices. Price is one example. Perhaps CleanAll is much more expensive than competitors. Even such
seemingly inane factors as scent could influence companies’ choice as to whether they should use
CleanAll. In short, the argument assumes that sick days and dirt removal account for the entirety of the
factors influencing companies’ decision in which cleanser to adopt; if these assumptions prove
unwarranted the argument will have far fewer grounds to stand on in its conclusions.
In sum, while the evidence cited in this argument does provide initial reasons to believe that CleanAll
is a superior cleanser, the argument’s conclusion rests on other assumptions—namely, that the cleaning
studies it cites are valid and reliable, that the use of “sick days” is an appropriate measure of cleanser
effectiveness, and that companies care about only these two things. Without concrete evidence that these
assumptions can be satisfied, we must remain skeptical.

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