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Estimated reading time: 1 minutes
Most people are familiar with statistical hypothesis tests such as the t-test and ANOVA to analyze whether two or more samples (from a parametric distribution) came from the same population. The nonparametric equivalents (Wilcoxon and Kruskal-Wallis tests) are less familiar but equally robust. What is not always clear is that these models are applied to one or more response variables; e.g., chemical concentrations that result from natural or anthropogenic causes. They do not answer the question of why these values were observed.
Regulators, stake holders, and environmental NGOs question the potential for adverse project effects on natural ecosystems, particularly surface waters, fish, and wildlife. These concerns are expressed at all stages of a project’s life cycle. One aspect of effectively addressing these concerns requires including explanatory and response variables in the statistical model to estimate how much response variability is explained by each explanatory variable. Which statistical model to apply depends on the specifics of the concern and the nature of the data (e.g., chemical or biological).
This work was originally published on the Applied Ecosystem Services, LLC web site at https://www.appl-ecosys.com/blog/explaining-environmental-data/
It is offered under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license. In short, you may copy and redistribute the material in any medium or format as long as you credit Dr. Richard Shepard as the author. You may not use the material for commercial purposes, and you may not distribute modified versions.
