ABSTRACT
The complexity/randomness of soil systems poses a formidable challenge to the design, analysis, and interpretation of microbiological experiments. This chapter reviews the classical quantitative methods and their application in soil science as well as advanced highly promising approaches such as machine learning and variations of artificial intelligence. It presents the key concepts and definitions related to the quantitative analyses of data. The chapter discusses specific examples to illustrate the type of statistics that can be used to characterize microbes, their activities, and their impacts on the environment. Graphs are used to visually summarize complex data sets using shapes and colors to represent quantities and lines and lettering to emphasize patterns. There are two types of statistical tests, parametric and nonparametric, which differ in the assumptions they make about the distribution of the measured variable in the underlying population. In many studies, univariate statistical tests will be sufficient for investigation, but in some instances, multivariate analyses are preferable.
