What type of statistical test is most appropriate for detecting trends in groundwater monitoring data?

The most appropriate type of statistical test for detecting trends in groundwater monitoring data is a non-parametric trend test, specifically the Mann-Kendall test. Groundwater monitoring data often exhibit non-normal distributions, seasonality, and censored data (values below the detection limit), which violate the assumptions of parametric tests like linear regression. The Mann-Kendall test is a non-parametric test that does not require the data to be normally distributed and can effectively handle censored data by assigning them a common value. It assesses whether there is a statistically significant monotonic trend (increasing or decreasing) over time. If the Mann-Kendall test indicates a significant trend, the Sen's slope estimator can then be used to estimate the magnitude of the trend. This combination of tests provides a robust and reliable method for identifying and quantifying trends in groundwater quality data, which is crucial for assessing the effectiveness of remediation efforts and ensuring compliance with regulatory standards. Using a parametric test on non-parametric data will lead to unreliable results.