The ghost of selective inference in spatiotemporal trend analysis.

In spatiotemporal trend analysis, selective inference occurs when researchers are only interested in significant trends based on a fixed threshold (α, often 0.05), without considering the total number of statistical tests performed. Using simultaneous inference in gridded data involves thousands of trend tests, one for each pixel, leading to multiple testing or multiplicity problems.

Multiplicity Eludes Peer Review: The Case of COVID-19 Research.

Multiplicity arises when data analysis involves multiple simultaneous inferences, increasing the chance of spurious findings. It is a widespread problem frequently ignored by researchers. In this paper, we perform an exploratory analysis of the Web of Science database for COVID-19 observational studies.

Increased risk of COVID-19 from walking dogs? Most likely, a spurious finding.

We analyse the paper "The spread of SARS-CoV-2 in Spain: Hygiene habits, sociodemographic profile, mobility patterns and comorbidities" authored by Rodríguez-Barranco et al. (2021), published in Environmental Research, vol.192, January 2021.

On the usefulness of the bioclimatic correlative models of SARS-CoV-2.

This paper addresses the effects of atmospheric conditions on the spread of the SARS-CoV-2 coronavirus and its associated disease, COVID-19. For this purpose, we assess the limitations of bioclimatic correlative models to explain the geographic distribution of SARS-CoV-2 in the context of medical geography. Overall, there is a broad consensus that the global distribution of COVID-19 is not random but conditioned by environmental drivers.