Abstract
If you are comparing chemical concentrations between a background area and a potentially contaminated area, how different do the concentrations in these two areas have to be before you decide that the potentially contaminated area is in fact contaminated? In the last chapter we showed how to use prediction and tolerance intervals to try to answer this question. There are other kinds of hypothesis tests you can use as well. R contains several functions for performing classical statistical hypothesis tests, such as t-tests, analysis of variance, linear regression, nonparametric tests, quality control procedures, and time series analysis (see the R documentation and help files). EnvStats contains modifications of some of these functions (e.g., summaryStats and stripChart), as well as functions for statistical tests that are not included in R but that are used in environmental statistics, such as the Shapiro-Francia goodness-of-fit test, Kendall’s seasonal test for trend, and the quantile test for a shift in the tail of the distribution (see the help file Hypothesis Tests). This chapter discusses these functions. See Millard et al. (2014) for a more in-depth discussion of hypothesis tests.
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Millard, S.P. (2013). Hypothesis Tests. In: EnvStats. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8456-1_7
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