From the 1990s climate change has decreased cool season catchment precipitation reducing river heights in Australia’s southern Murray-Darling Basin

The Murray-Darling Basin (MDB) is Australia’s major agricultural region. The southern MDB receives most of its annual catchment runoff during the cool season (April–September). Focusing on the Murrumbidgee River measurements at Wagga Wagga and further downstream at Hay, cool season river heights are available year to year. The 27-year period April–September Hay and Wagga Wagga river heights exhibit decreases between 1965 and 1991 and 1992–2018 not matched by declining April-September catchment rainfall. However, permutation tests of means and variances of late autumn (April–May) dam catchment precipitation and net inflows, produced p-values indicating a highly significant decline since the early 1990s. Consequently, dry catchments in late autumn, even with average cool season rainfall, have reduced dam inflows and decreased river heights downstream from Wagga Wagga, before water extraction for irrigation. It is concluded that lower April–September mean river heights at Wagga Wagga and decreased river height variability at Hay, since the mid-1990s, are due to combined lower April–May catchment precipitation and increased mean temperatures. Machine learning attribute detection revealed the southern MDB drivers as the southern annular mode (SAM), inter-decadal Pacific oscillation (IPO), Indian Ocean dipole (IOD) and global sea-surface temperature (GlobalSST). Continued catchment drying and warming will drastically reduce future water availability.

Choosing Between the Two-sample T Test and its Alternatives: A Practical Guideline

The traditional guideline for choosing between the two-sample $t$ test and its alternatives is primarily based on assessing assumptions. Many flaws of this approach have been documented. In this paper, I address those flaws briefly and propose a new guideline for choosing between the two-sample $t$ test and its alternatives. I propose to select the hypothesis that operationalizes the research question best. This selection is carried out before data collection and entails identifying the hypotheses that in principle produce meaningful results, and among those, the most appropriate one. I advise to not only report on the most appropriate hypothesis but also on the remaining meaningful hypotheses, as they provide valuable complementary information. For testing the selected hypotheses, I recommend bootstrap and permutation tests instead of the traditionally used parametric tests. The role of assessing assumptions is downgraded to deciding whether the results of a test are reliable. An important implication of the proposed guideline is that in most cases, a nonparametric permutation test should be used instead of the $t$ test.

Common Permutation Methods in Animal Social Network Analysis Do Not Control for Non-independence

The non-independence of social network data is a cause for concern among behavioural ecologists conducting social network analysis. This has led to the adoption of several permutation-based methods for testing common hypotheses. One of the most common types of analysis is nodal regression, where the relationships between node-level network metrics and nodal covariates are analysed using a permutation technique known as node-label permutation. We show that, contrary to accepted wisdom, node-label permutations do not account for the types of non-independence assumed to exist in network data, because regression-based permutation tests still assume exchangeability of residuals. The same theoretical condition also applies to the quadratic assignment procedure (QAP), a permutation-based method often used for conducting dyadic regression. We highlight that node-label permutations produce the same p-values as equivalent parametric regression models, but that in the presence of confounds, parametric regression models produce more accurate effect size estimates. We also note that QAP only controls for a specific type of non-independence between edges that are connected to the same nodes, and that appropriate parametric regression models are also able to account for this type of non-independence. Based on this, we advocate the retirement of permutation tests for regression analyses, in favour of well-specified parametric models. Moving away from permutation-based methods will reduce over-reliance on p-values, generate more reliable estimates of effect sizes, and facilitate the adoption of more powerful types of statistical analysis.

Functional Symmetry and Statistical Depth for the Analysis of Movement Patterns in Alzheimer’s Patients

Black-box techniques have been applied with outstanding results to classify, in a supervised manner, the movement patterns of Alzheimer’s patients according to their stage of the disease. However, these techniques do not provide information on the difference of the patterns among the stages. We make use of functional data analysis to provide insight on the nature of these differences. In particular, we calculate the center of symmetry of the underlying distribution at each stage and use it to compute the functional depth of the movements of each patient. This results in an ordering of the data to which we apply nonparametric permutation tests to check on the differences in the distribution, median and deviance from the median. We consistently obtain that the movement pattern at each stage is significantly different to that of the prior and posterior stage in terms of the deviance from the median applied to the depth. The approach is validated by simulation.