This course aims to give students a broad understanding of nonparametric statistical 
methods; that is, what types of statistical analysis can be carried out either when the 
samples are small, or when standard normality assumptions do not hold.  Students will 
first learn about tests based on the binomial distribution, as these are likely to be 
most familiar from previous statistics classes.  From this starting point, students 
will expand their knowledge base to include a number of relevant practical situations, 
such as the analysis of categorical data arranged in arrays.  We will explore the 
trade-offs between nonparametric and parametric approaches in terms of statistical 
power, ease of computation, and other  criteria.  Students will also be exposed to 
"modern nonparametric" methods, such as the bootstrap.  

Students will learn how to use specific statistical methods and general modes of 
statistical thinking to make inferences from data, and to support (or refute) an 
argument or point of view with quantitative information.  Students will learn the 
mathematical and probabilistic underpinnings of statistical methods, they will develop 
an understanding  of the underlying rationale for specific statistical methods, and 
they will learn how to assess the relative merits and applicability of competing 
statistical techniques.  

Students will analyze data using the methods learned in lecture, with appropriate 
statistical software. Some amount of independent programming will be necessary as not 
all nonparametric procedures covered over the course of the semester are "built in" to 
existing software packages.

Tests based on the binomial distribution.  Contingency table analysis.  Methods based 
on ranks.  Goodness of fit statistics (Kolmogorov-Smirnov and related tests).  Tests 
based on runs. Modern nonparametric statistics (smoothing, bootstrap).