Biostatistics with R An Introductory Guide for Field Biologists

We start by comparing the use of correlation (namely the Pearson linear correlation coefficient), for describing the relationship of two variables, with the approach based on a simple linear regression. Then we describe how to test the hypothesis that there is no correlation between the two variables within the sampled population. We conclude this topic by discussing the power of this test. We then move to the nonparametric correlation coefficients suitable for measuring the strength of a monotonic relationship between two variables. An additional section focuses on how to appropriately interpret correlation strength and significance, factoring in the specific questions being asked. Finally, we discuss the differences between correlation-based and causal relationships. The methods described in this chapter are accompanied by a carefully-explained guide to the R code needed for their use, including the pwr package.