I'm pretty new to statistics. I have a temporal data set consisting of nitrate levels in water collected from 5 different locations. (I wanted to correlate it with water pH in the region in long run.) Based on box plots, it appears that level of nitrates in location A and C are significantly higher than other spots at a given month. I would like to prove that A and C contributes most to the total nitrate levels of that month than other spots by a hypothesis testing and p-Values. Is there any hypothesis testing or post hoc test to do that. (The data ins not normally distributed) Thank you.
The Bonferroni is probably the most commonly used post hoc test, because it is highly flexible, very simple to compute, and can be used with any type of statistical test (e.g., correlations)—not just post hoc tests with ANOVA.
A few thoughts:
- Nitrate concentrations in environmental waters are often log-normally distributed. For more complex analyses, you might log transform your concentration data.
- It sounds like you have observations paired by sample date. Or at least by month. If this is the case --- and you have one observation per date and location --- a simple test is the Quade test, which blocks observations. (Quade test is similar in use to Friedman test, but is a better test for continuous data.) There are post-hoc tests specific for Quade test (https://www.rdocumentation.org/packages/PMCMR/versions/4.3/topics/posthoc.quade.test).
Dear Good Sirs,
Thank you very much for your information. Really appreciate your answers.
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