By David Etherton
A writer named Dale Carnegie once said, “Take a chance! All life is a chance. The man who goes farthest is generally the one who is willing to do and dare.” When you think about it, he is absolutely right. Everything we do in life is by chance. Whether you are starting a business or starting a rock band, running in a marathon or running for president, playing poker or playing slot machines, everything we do involves the chance that we may not succeed. It may be for this reason that a whole subfield of mathematics has been dedicated solely to chance; this subfield is known as probability theory.
Probability theory has existed for at least 350 years, although many believe that it has been around much longer than this (Burton, 2011, p. 439). This field originated from two different subjects: analyzing data to determine death & insurance rates (statistics), and how to win at games that are solely based on chance (probability) (Burton, 2011, p. 439).
To begin, the man who first laid the groundwork for the field of statistics was John Gaunt (Burton, 2011, p. 440). Gaunt, who worked as a merchant in London, was the earliest person to use a large set of data in making a set of statistics (Burton, 2011, p. 440). In 1662, he created a work that tried to categorize the causes of death among females and males in London (Burton, 2011, p. 440). When he saw that his work was not being utilized to its full potential, however, he decided to take matters into his own hands (Burton, 2011, p. 440). To put his work to good use, Gaunt collected over 50 years of data, in fact, and then analyzed it to see the mortality rates for people at different ages (Burton, 2011, p. 440).
Now, for the field of probability, we need to go a little bit further back in time. It is said that people started playing games of chance (gambling) over 5000 years ago, and that the first dice were invented around 3000 BC in Iraq (Burton, 2011, p. 443). The invention of playing cards is a little bit hazy, as it is said that the Chinese, Egyptians, and Indians may have all created it (Burton, 2011, p. 444). Much later on during in the 16th century, a man by the name of Girolamo Cardan wrote a book called Liber de Ludo Aleae, which not only defined probability, but was also the first source to relate probability to games of chance (Burton, 2011, p. 445). It is not Cardan, however, who is said to have revolutionized probability theory. In 1654, a man named Chevalier de Méré was concerned with questions of gambling, so he sent his inquiries to Blaise Pascal (Burton, 2011, p. 454). Pascal, in turn, wrote Pierre de Fermat to help him with these problems (Burton, 2011, p. 454). They worked together on the probability involved in throwing dice and, as a result of their combined efforts, these two people are known to have developed probability theory (Burton, 2011, p. 455).
After all these years, probability and statistics hold a fine place in mathematics, and in my opinion, it is because there are many so different ways to apply this topic to the real world, and, over the years, probability and statistics have evolved quite a bit and are now the focus of many careers. An example of one of these careers would be an actuary, who gets paid to determine how much risk is involved in various situations, how much money could be lost or made during these situations. Another example would be statisticians (big surprise, right?), which collect data and analyze it by various means. There are many careers that revolve around probability and statistics, and one thing worth mentioning about many of these jobs is that they can pay very well. According to the Bureau of Labor Statistics (2009), actuaries made a median salary of over $84,000 in the year of 2008, and some actually made over $160,000. The Bureau of Labor Statistics (2009) also states that statisticians were making a median salary of about $72,000 in 2008, with some easily breaking the six-figure mark.
For more information about what these types of careers entail, you may want to visit the following websites.
http://www.amstat.org/careers/
http://www.beanactuary.org/
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, 2010-11 Edition, Actuaries, on the Internet at http://www.bls.gov/oco/ocos041.htm (visited November 10, 2011).
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, 2010-11 Edition, Statisticians, on the Internet at http://www.bls.gov/oco/ocos045.htm (visited November 10, 2011).
Burton, D. M. (2011). The development of probability theory: Pascal, Bernoulli, and Laplace. In
The history of mathematics (Seventh ed., pp. 439-496). New York, NY: McGraw-Hill. (Original work published 1999)
Chance quotes. (n.d.). Retrieved from http://www.brainyquote.com/quotes/keywords/chance_2.html
A writer named Dale Carnegie once said, “Take a chance! All life is a chance. The man who goes farthest is generally the one who is willing to do and dare.” When you think about it, he is absolutely right. Everything we do in life is by chance. Whether you are starting a business or starting a rock band, running in a marathon or running for president, playing poker or playing slot machines, everything we do involves the chance that we may not succeed. It may be for this reason that a whole subfield of mathematics has been dedicated solely to chance; this subfield is known as probability theory.
Probability theory has existed for at least 350 years, although many believe that it has been around much longer than this (Burton, 2011, p. 439). This field originated from two different subjects: analyzing data to determine death & insurance rates (statistics), and how to win at games that are solely based on chance (probability) (Burton, 2011, p. 439).
To begin, the man who first laid the groundwork for the field of statistics was John Gaunt (Burton, 2011, p. 440). Gaunt, who worked as a merchant in London, was the earliest person to use a large set of data in making a set of statistics (Burton, 2011, p. 440). In 1662, he created a work that tried to categorize the causes of death among females and males in London (Burton, 2011, p. 440). When he saw that his work was not being utilized to its full potential, however, he decided to take matters into his own hands (Burton, 2011, p. 440). To put his work to good use, Gaunt collected over 50 years of data, in fact, and then analyzed it to see the mortality rates for people at different ages (Burton, 2011, p. 440).
Now, for the field of probability, we need to go a little bit further back in time. It is said that people started playing games of chance (gambling) over 5000 years ago, and that the first dice were invented around 3000 BC in Iraq (Burton, 2011, p. 443). The invention of playing cards is a little bit hazy, as it is said that the Chinese, Egyptians, and Indians may have all created it (Burton, 2011, p. 444). Much later on during in the 16th century, a man by the name of Girolamo Cardan wrote a book called Liber de Ludo Aleae, which not only defined probability, but was also the first source to relate probability to games of chance (Burton, 2011, p. 445). It is not Cardan, however, who is said to have revolutionized probability theory. In 1654, a man named Chevalier de Méré was concerned with questions of gambling, so he sent his inquiries to Blaise Pascal (Burton, 2011, p. 454). Pascal, in turn, wrote Pierre de Fermat to help him with these problems (Burton, 2011, p. 454). They worked together on the probability involved in throwing dice and, as a result of their combined efforts, these two people are known to have developed probability theory (Burton, 2011, p. 455).
After all these years, probability and statistics hold a fine place in mathematics, and in my opinion, it is because there are many so different ways to apply this topic to the real world, and, over the years, probability and statistics have evolved quite a bit and are now the focus of many careers. An example of one of these careers would be an actuary, who gets paid to determine how much risk is involved in various situations, how much money could be lost or made during these situations. Another example would be statisticians (big surprise, right?), which collect data and analyze it by various means. There are many careers that revolve around probability and statistics, and one thing worth mentioning about many of these jobs is that they can pay very well. According to the Bureau of Labor Statistics (2009), actuaries made a median salary of over $84,000 in the year of 2008, and some actually made over $160,000. The Bureau of Labor Statistics (2009) also states that statisticians were making a median salary of about $72,000 in 2008, with some easily breaking the six-figure mark.
For more information about what these types of careers entail, you may want to visit the following websites.
http://www.amstat.org/careers/
http://www.beanactuary.org/
Bibliography
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, 2010-11 Edition, Actuaries, on the Internet at http://www.bls.gov/oco/ocos041.htm (visited November 10, 2011).
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, 2010-11 Edition, Statisticians, on the Internet at http://www.bls.gov/oco/ocos045.htm (visited November 10, 2011).
Burton, D. M. (2011). The development of probability theory: Pascal, Bernoulli, and Laplace. In
The history of mathematics (Seventh ed., pp. 439-496). New York, NY: McGraw-Hill. (Original work published 1999)
Chance quotes. (n.d.). Retrieved from http://www.brainyquote.com/quotes/keywords/chance_2.html
