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Introduction
:
Statistics can be defined as “the collection, organization, and analysis of
numerical data” (Singer).
This branch of mathematics has many uses in everyday life, such as
analyzing surveys to determine the effectiveness of television commercials or using raw
experimental data to predict the overall impact of a drug’s side effects.
Today, surveys are one
of the most common forms of statisticallyanalyzed data.
One can also view statistics as an
integral part of any scientific field, particularly in biology and the study of populations.
Furthermore, this field has been useful to civilization for more than 5000 years.
The
Babylonians used a simplified form of statistics to keep track of trade, and the Egyptians also
analyzed their wealth and populations around the same time.
Even the Romans took censuses of
their citizens and used the statistical information for taxation purposes.
In the seventeenth
century, Blaise Pascal and Pierre de Fermat explored the related field of probability as it related
to gambling.
The study of probability, which examines the effects of chance, led to a greater
sophistication in statistical techniques.
For example, William Sealy Gosset developed the
student’s ttest under the pseudonym Student while he was working in a local brewery in 1908.
The student’s ttest compares a calculated tvalue to a critical value; if the tvalue is greater than
or equal to the critical value, one may reject the null hypothesis (a statement of no difference that
defines what the ttest is supposed to prove or disprove).
The purpose of this test is to determine
the likelihood that chance accounted for the data results.
Around the same time, Karl Pearson
developed the chi square test, another measure of the significance of data that involves the
attempt to reject a null hypothesis.
This test relies on the assumptions that the data points are
mutually exclusive and that the data are ordered on a nominal scale.
He also developed the
product moment correlation, a measure of how closely two sets of data are related to each other.
Again, it revolves around the rejection of a null hypothesis by means of comparing a calculated
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View Full Documentvalue with a critical value.
Finally, one last form of statistical test is called linear regression.
Although the underlying theory was developed independently by Gauss and Legendre in the
early nineteenth century, it was later expanded by mathematicians such as Karl Pearson and
Francis Galton, a biologist.
Today, linear regression is a widespread statistical test used to
determine if one set of data is dependent on another set.
In modern times, the field of statistics forms a basis of everyday life because it
encompasses many different subjects.
Meteorology reports, doctors’ case studies, and gambling
are all closely associated with statistics.
Despite its ancient roots, this branch of mathematics is
still incredibly relevant today.
Methods and Materials
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 Spring '11
 Smith
 Biology

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