C.6 Study Question 6: Is there seasonality in the concentrations?

Most statistical tests assume statistical independence of the sample data. Temporally dependent groundwater data violate the assumption of independence. High levels of variability unrelated to the long-term temporal trend also make it difficult to identify statistically significant long-term trends and to estimate attenuation rates and remediation time frames (McHugh et al. 2011).

When temporal variability exists because of the distribution of the timing of the sample collection, the distribution exhibits time dependence or autocorrelationCorrelation of values of a single variable data set over successive time intervals (Unified Guidance). The degree of statistical correlation either (1) between observations when considered as a series collected over time from a fixed sampling point (temporal autocorrelation) or (2) within a collection of sampling points when considered as a function of distance between distinct locations (spatial autocorrelation). (for example, a cyclical pattern of data affected by the seasons). To verify statistical independence, demonstrate a low correlationAn estimate of the degree to which two sets of variables vary together, with no distinction between dependent and independent variables (USEPA 2013b). between the concentration and the time of the sampling event. Evaluate the cyclical change (seasonal variation) to adequately understand the varianceThe square of the standard deviation (EPA 1989); a measure of how far numbers are separated in a data set. A small variance indicates that numbers in the dataset are clustered close to the mean. of the population. A cyclical pattern can biasSystematic deviation between a measured (observed) or computed value and its true value. Bias is affected by faulty instrument calibration and other measurement errors, systematic errors during data collection, and sampling errors such as incomplete spatial randomization during the design of sampling programs (Unified Guidance). the variance of the distribution or create a slope of the concentration that is not monotonic. You can evaluate the temporal evaluation and adjust the distribution to evaluate the trend accordingly.

This question is usually relevant in the remediation, monitoring, and closure stages of the project life cycle.

Selecting and Characterizing the Data Set

Refer to Section 3.4: Common Statistical Assumptions for further discussion of how the following requirements may affect statistical analysis results.

If the test does not assume a distribution, then no testing of the distribution is necessary. However, if a substantial number of nondetectsLaboratory analytical result known only to be below the method detection limit (MDL), or reporting limit (RL); see "censored data" (Unified Guidance). are present, then the test cannot indicate autocorrelation. The samples should cover multiple years with an observable seasonal pattern each year. Each season should include at least three measurements.

When the objective is to determine if there is a temporal change or pattern to the data, simple graphical procedures can reveal significant trends. However, if cyclical effects complicate the pattern of the data then consider other statistical methods listed below to answer this study question.

Statistical Methods and Tools

Determine if there is a significant cyclical pattern in the data that creates autocorrelation between the samples; statistical independence of the data is a key assumption for many statistical tests. When the objective is to determine if there is a temporal change or pattern to single series data, use the sample autocorrelation function or Rank von Neumann ratio test to identify correlated samples from specific seasons. When the objective is to determine if there is a temporal change or pattern to a group of wells, use time series plots or the Kruskal-Wallis test to identify correlated samples related to specific seasons.

Sample Autocorrelation Function

Rank von Neumann Ratio Test

Time Series Plots

Kruskal-Wallis Test

Interpretation of Results and Associated Uncertainty

The temporal trend analyses explain whether and how contaminant concentrations are changing over time. As discussed above, a nonparametric test such as the seasonal Mann-Kendall test does not require assumptions regarding the data distribution. However, significant seasonal fluctuations in the data will cause false negativeIn hypothesis testing, if the alternative hypothesis (Hᴀ) is true but is rejected in favor of the null hypothesis (H₀) which is not true, then a false negative (Type II, β) error has occurred (Unified Guidance). errors in the statistical test methods. Temporally dependent and autocorrelated data generally contain both a truly random and nonrandom component. Only strong correlations are likely to affect the results of further statistical testing. See also Section 4.5.1 and Section 4.6.2.

Related Study Questions                                                                                                      

Study Question 5: Is there a trend in contaminant concentrations?

Key Words: Temporal Trends, Remediation, Monitoring, Closure, Cyclic or Periodic Change


Publication Date: December 2013

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