Why multiple hypothesis test corrections provide poor control of false positives in the real world.

Most scientific disciplines use significance testing to draw conclusions about experimental or observational data. This classical approach provides a theoretical guarantee for controlling the number of false positives across a set of hypothesis tests, making it an appealing framework for scientists seeking to limit the number of false effects or associations that they claim to observe. Unfortunately, this theoretical guarantee applies to few experiments, and the true false positive rate (FPR) is much higher.

Chemical contaminants in donkey milk: A review of literature on sources, routes and pathways of contamination, regulatory framework, health risks, and preventive measures.

Donkey milk has garnered increasing attention due to its potential health benefits and nutritional properties, positioning it as a valuable alternative to cow's milk for specific consumer groups, such as individuals with allergies, young children, elderly populations, and those with compromised immune systems. However, the presence of chemical contaminants in donkey milk presents a significant concern for food safety and public health. This review aims to provide an assessment of the types and sources of chemical contaminants in donkey milk, including heavy metals, mycotoxins, pesticides, polychlorinated biphenyls, and antimicrobial and antiparasitic veterinary drugs.

Ditching the norm: Using alternative distributions for biological data analysis.

Most classical statistical tests assume data are normally distributed. If this assumption is not met, researchers often turn to non-parametric methods. These methods have some drawbacks, and if no suitable non-parametric test exists, a normal distribution may be used inappropriately instead.

Causal mediation analysis: How to avoid fooling yourself that causes .

The purpose of many preclinical studies is to determine whether an experimental intervention affects an outcome through a particular mechanism, but the analytical methods and inferential logic typically used cannot answer this question, leading to erroneous conclusions about causal relationships, which can be highly reproducible. A causal mediation analysis can directly test whether a hypothesised mechanism is partly or completely responsible for a treatment's effect on an outcome. Such an analysis can be easily implemented with modern statistical software.

Evaluation of longitudinal passive immunity transfer against lumpy skin disease virus in calves by different serological methods.

To implement effective lumpy skin disease (LSD) control measures, such as timely vaccination, particularly in calves and serological monitoring, it is necessary to evaluate immune response after vaccination, both in adult cattle and in their calves. The aim of this study was to evaluate passive immunity transfer and duration of maternal antibodies against lumpy skin disease virus (LSDV) in calves born to vaccinated cows by two different serological methods. The longitudinal study was carried out on two farms in Serbia where no cases were reported during LSD outbreak in 2016.