Environmental data arise when studying ecosystems, pollution, plant or animal populations, climate, and other aspects of the natural environment. Environmental data can be observational or experimental. Environmental data can result from laboratory analysis of field samples. Environmental data often leave the investigator scratching his or her head and wondering, ``So now what do I do with these data?"

Environmental statistcs is simply statistics applied to environmental data. In particular, environmental statistics has come to mean a collection of methods that attack problems that arise over and over again in environmental data.

This course will survey a number of topics from environmental statistics, include sampling, working with censored data, change detection, time series, and spatial statistics. For example, laboratory analysis of a sample may lead to a ``non-detect;" it's not a zero, it's not a number, what do you do with it? As another example, the first law of geography says that everything is related, but nearby things are more related; spatial statistics works with these spatial relationships. Most of the topics we'll cover are situations where the ``standard methods'' in statistics that assume independence, normality, and constant variance can fail rather badly.

Clearly, we will not be able to go into any one topic in great depth, but after this course you should be able to recognize the issues, know the basic approaches, and know where to go for more information.