**Course** Description: | Techniques and applications of nonparametric statistical methods, estimates, confidence intervals, one sample tests, two sample tests, several sample tests, tests of fit, nonparametric analysis of variance, correlation tests, chi-square test of independence and homogeneity, sample size determination for some nonparametric tests. |

**Course Objectives:** | 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. |