Statistics true data. The statistical battle in the

Statistics are used to describe
data and draw conclusions.  However, most
often, it can be manipulated in such way as to mislead people into believing
far from the truth.  In aviation,
statistics are commonly used to compare accidents in order to claim which
aircraft is safer or whether the aviation industry altogether is safe compared
to other transportation.  However,
statistics are numbers, and if those numbers are presented a strategic way, it
can benefit or hurt the competition. 

when comparing aviation accident statistics, there are many factors to consider
and many angles to look at in order to filter through the jargon and reach the
true data.  The statistical battle in the
aviation industry ranges aircraft manufacturers, airlines, and even the
aviation industry altogether, all trying to prove why they are better and
safer.  The aviation industry is often
compared in safety to driving.  However,
the comparison is not fair in all sides to be considered accurate.  There are many angles that need to be covered
in this comparison.  For example,
aviation accident stats use flight hours versus in vehicles, you would use
miles.  Should aircraft accident stats be
presented in miles as well?  How would
this change the perspective? 

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            Suppose we
used flight hours in aircraft accident statistics versus miles in car accident
statistics.  Using the Nall Report of
2005, 7.2 accidents and 1.39 fatalities occur in general aviation every 100,000
flight hours.  In comparison, there are
1.3 deaths every 100 million miles on the road (Ison, 2007).  Now, taking the same statistics and changing
the measurements from flight hours to miles instead, that number would change
to 16.3 deaths every 100 million miles. 
Comparing that to the motor vehicle rate of 1.3, aviation seems quite
dangerous (Ison, 2007).  Now, lets take a
look at a different angle, what are the chances of death in general aviation
versus on the road?  The chances of
fatality according to same report in 2005 were one in 613,000.  Chances of dying on the road were one in
7,700 (Ison, 2007).  Now we see things

            So, what
are we not taking into consideration? 
Really, it’s the fact that you are comparing apples to oranges.  It is very hard to determine whether these
stats can accurately assess safety when comparing two different
industries.  You might say, well lets
compare aircraft within the industry. 
Well, no you are getting closer, however, still a lot to take into
account.  For example, aircraft are used
for different purposes; you have passenger aircraft, cargo aircraft, leisure
aircraft, training, private, etc. 
Therefore, all have a different amount of exposure.  Meaning that amount of opportunity for an
accident/event to occur is not the same (Rodriguez & Cusick, 2012).  This could be because the use, situations,
routes, and capabilities are all different. 
You can’t compare an aircraft traveling internationally to one traveling
an hour in the states or an aircraft only flown on good weather versus one
operational at all times.  There must be
a level playing field and a equal level of risk and exposure in order to
consider statistics to be accurate in a comparison (Rodriguez & Cusik,

the factors to be aware of when comparing and analyzing aviation accident
statistics are, as mentioned previously, different industry, the use of
measurements, word jargon, and most importantly, exposure.  Statistics can play a foul game if not
deciphered properly.  They can be easily
manipulated and very deceptive to those who do not seek beyond what is
presented.  Therefore, making an
assumption after the first stat thrown to you can be a mistake.  Especially in the environment we live in
today, where wordplay seems to have an advantage over facts, its is important
to sift through all the numbers thrown at you and seek the truth.  Always, do your own research, and don’t be
fooled by the “fake news”. 



Rodrigues, & Cusick, S. K. (2012). Commercial aviation
safety. New York, N.Y: McGraw-Hill Education LLC.


Ison, D. (2007). Deciphering Accident Statistics. Retrieved
January 26, 2018, from