Abstract. The Amazon basin is a vast continental area in which atmospheric composition is relatively unaffected by anthropogenic aerosol particles. Understanding the properties of the natural biogenic aerosol particles over the Amazon rainforest is key to understanding their influence on regional and global climate. While there have been a number of studies during the wet season, and of biomass burning particles in the dry season, there has been relatively little work on the transition period -the start of the dry season in the absence of biomass burning. As part of the Brazil-UK Network for Investigation of Amazonian Atmospheric Composition and Impacts on Climate (BUNIAACIC) project, aerosol measurements, focussing on unpolluted biogenic air masses, were conducted at a remote rainforest site in the central Amazon during the transition from wet to dry season in July 2013. This period marks the start of the dry season but before significant biomass burning occurs in the region.Median particle number concentrations were 266 cm −3 , with size distributions dominated by an accumulation mode of 130-150 nm. During periods of low particle counts, a smaller Aitken mode could also be seen around 80 nm. While the concentrations were similar in magnitude to those seen during the wet season, the size distributions suggest an enhancement in the accumulation mode compared to the wet season, but not yet to the extent seen later in the dry season, when significant biomass burning takes place. Submicron nonrefractory aerosol composition, as measured by an aerosol chemical speciation monitor (ACSM), was dominated by organic material (around 81 %). Aerosol hygroscopicity was probed using measurements from a hygroscopicity tandem differential mobility analyser (HTDMA), and a quasi-monodisperse cloud condensation nuclei counter (CCNc). The hygroscopicity parameter, κ, was found to be low, ranging from 0.12 for Aitken-mode particles to 0.18 for accumulation-mode particles. This was consistent with previous studies in the region, but lower than similar measurements conducted in Borneo, where κ ranged 0.17-0.37.A wide issue bioaerosol sensor (WIBS-3M) was deployed at ground level to probe the coarse mode, detecting primary biological aerosol by fluorescence (fluorescent biological aerosol particles, or FBAPs). The mean FBAP number concentration was 400 ± 242 L −1 ; however, this ranged from around 200 L −1 during the day to as much as 1200 L −1 at night. FBAPs dominated the coarse-mode particles, comprising between 55 and 75 % of particles during the day to more than 90 % at night. Non-FBAPs did not show a strong diurnal pattern. Comparison with previous FBAP measurements above canopy at the same location suggests there is a strong vertical gradient in FBAP concentrations through the canopy. Cluster analysis of the data suggests that FBAPs were dominated (around 70 %) by fungal spores. Further, long-term measurements will be required in order to fully examine the seasonal variability and distribution of primary biological aerosol parti...

Simultaneous hypothesis tests can fail to provide results that meet logical requirements. For example, if A and B are two statements such that A implies B, there exist tests that, based on the same data, reject B but not A. Such outcomes are generally inconvenient to statisticians (who want to communicate the results to practitioners in a simple fashion) and non-statisticians (confused by conflicting pieces of information). Based on this inconvenience, one might want to use tests that satisfy logical requirements. However, Izbicki and Esteves shows that the only tests that are in accordance with three logical requirements (monotonicity, invertibility and consonance) are trivial tests based on point estimation, which generally lack statistical optimality. As a possible solution to this dilemma, this paper adapts the above logical requirements to agnostic tests, in which one can accept, reject or remain agnostic with respect to a given hypothesis. Each of the logical requirements is characterized in terms of a Bayesian decision theoretic perspective. Contrary to the results obtained for regular hypothesis tests, there exist agnostic tests that satisfy all logical requirements and also perform well statistically. In particular, agnostic tests that fulfill all logical requirements are characterized as region estimator-based tests. Examples of such tests are provided.

This paper introduces pragmatic hypotheses and relates this concept to the spiral of scientific evolution. Previous works determined a characterization of logically consistent statistical hypothesis tests and showed that the modal operators obtained from this test can be represented in the hexagon of oppositions. However, despite the importance of precise hypothesis in science, they cannot be accepted by logically consistent tests. Here, we show that this dilemma can be overcome by the use of pragmatic versions of precise hypotheses. These pragmatic versions allow a level of imprecision in the hypothesis that is small relative to other experimental conditions. The introduction of pragmatic hypotheses allows the evolution of scientific theories based on statistical hypothesis testing to be interpreted using the narratological structure of hexagonal spirals, as defined by Pierre Gallais.

In randomized experiments, a single random allocation can yield groups that differ meaningfully with respect to a given covariate. Furthermore, it is unfeasible to control the allocation with respect to a moderate number of covariates. As a response to this problem, Morgan and Rubin [1, 2] proposed an approach based on rerandomization to ensure that the final allocation obtained is balanced. However, despite the success of the Rerandomization method, it has an exponential computational cost in the number of covariates, for fixed balance constraints. Here, we propose the use of Hapzard Intentional Allocation, an alternative allocation method based on optimal balance of the covariates extended by random noise, see Lauretto et al [3]. Our proposed method can be divided into a randomization and an optimization step. The randomization step consists of creating new (artificial) covariates according a specified distribution. The optimization step consists of finding the allocation that minimizes a linear combination of the imbalance in the original covariates and the imbalance in the artificial covariates. Numerical experiments on real and simulated data show a remarkable superiority of Haphazard Intentional Allocation over the Rerandomization method, both in terms of balance between groups and in terms of inference power.

Although logical consistency is desirable in scientific research, standard statistical hypothesis tests are typically logically inconsistent. To address this issue, previous work introduced agnostic hypothesis tests and proved that they can be logically consistent while retaining statistical optimality properties. This article characterizes the credal modalities in agnostic hypothesis tests and uses the hexagon of oppositions to explain the logical relations between these modalities. Geometric solids that are composed of hexagons of oppositions illustrate the conditions for these modalities to be logically consistent. Prisms composed of hexagons of oppositions show how the credal modalities obtained from two agnostic tests vary according to their threshold values. Nested hexagons of oppositions summarize logical relations between the credal modalities in these tests and prove new relations.