The word "probability" in one form or
another is part of our daily vocabulary. In using it we articulate in casual way our subjective estimates of probability
regarding completion of deadlines, accomplishment of daily endeavors, departures and arrivals at work, play or
home. "Honey, I'll probably be late for dinner", "There's a good chance I will get a pay raise",
"If I get an MBA my chances of succeeding in the job market will increase substantially", and "I
will probably be there by noon", are all examples of our daily use of personal probability theory.
Davis et al. in Statistics and Research Methods for Managerial Decisions, p.219 tells us that there are two distinct
interpretations of the term probability. These interpretations are based on the methods through which we arrive
at conclusions about probability and possible outcomes.
The first method says Davis et al. makes assumptions about the physical world to determine the probability of a
particular outcome. If I assume that dice are made in a way such that they are likely to land on any of their six
surfaces and that the way in which I throw one die has absolutely no effect on how it lands I could conclude that
the probability of number six showing on top is one is six or 1/6.
The second method says Davis et al. consists of "observing the relative frequency over many, many repetitions
of the situations." In the case of the die, I would have to sit and throw it a significant number of times
and based on how it lands arrive at a conclusion regarding the probability that a specific occurrence or the way
in which it could land would indeed occur. Clearly, making such a determination on the basis of a few throws of
the die would only make me arrive at a poor conclusion.
In business, probability theory is used in the calculation of long-term gains and losses. This is how a company
whose business is based on risk calculates "probability of profitability" within acceptable margins.
An example of this is the way in which life insurance companies calculate the cost of life insurance policies and
is based on how many policy holders are reasonably expected to die within a year versus revenue generated from
other policies extended. In this scenario it is important to point out that in order for a company to mitigate
the risk associated with loss of revenue it must issue a substantial number of policies.
My decision to use of the throw of dice in this example is not probabilistic but chosen for a very specific reason
in describing the way in which I have seen business decisions made by companies. I propose that the level of technology
as a service offering to its customers and public ownership of the company are of substantial influence in making
decisions based on short and shallow analysis of their probability for success (some day I may take this one on
and try to conduct a study). You can see this on the news almost every day or by reading The Wall Street Journal.
Clearly, the bottom line in this economy is driven by expense, so much more in public owned companies where allocation
of resources to research and investigation are often perceived as frivolous by stockholders.
Davis et al. points out on page 300 Carl Sagan's belief the "probability of a major asteroid hitting the Earth
soon is high enough to be of concern." It is obvious that Sagan could not arrive at this conclusion through
the application of long-term frequency probability but rather on his knowledge of astronomy. This is the way in
which most businesses particularly in the Information Technology industry must make decisions these days - using
little research available, devoting small resources to discover answers and in a highly competitive environment.
My point is that if you can't invest the time and resources to conduct proper research, you may as well gamble
you company's money away shooting craps (dice).
Davis et al., Statistics and Research Methods for Managerial Decisions [University of Phoenix Custom Edition].
Ohio: South-Western, 2001.
Copyright © 2002-2006 Ed Zayas