Editorial, special issue on “Advances in Robust Statistics”

METRON ◽

10.1007/s40300-021-00213-w ◽

2021 ◽

Author(s):

Marco Riani ◽

Mia Hubert

Keyword(s):

Statistical Methods ◽

Robust Statistics ◽

Statistical Analyses ◽

Striking Feature ◽

Special Issue ◽

Current State ◽

Editorial Special Issue ◽

Data Points ◽

Robust Statistical Methods

AbstractStarting with 2020 volume, the journal Metron has decided to celebrate the centenary since its foundation with three special issues. This volume is dedicated to robust statistics. A striking feature of most applied statistical analyses is the use of methods that are well known to be sensitive to outliers or to other departures from the postulated model. Robust statistical methods provide useful tools for reducing this sensitivity, through the detection of the outliers by first fitting the majority of the data and then by flagging deviant data points. The six papers in this issue cover a wide orientation in all fields of robustness. This editorial first provides some facts about the history and current state of robust statistics and then summarizes the contents of each paper.

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Addressing Unusual Assay Variability with Robust Statistics

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/24725552211038379 ◽

2021 ◽

pp. 247255522110383

Author(s):

Jason Haelewyn ◽

Philip W. Iversen ◽

Jeffrey R. Weidner

Keyword(s):

Best Practices ◽

Statistical Methods ◽

Robust Statistics ◽

Biological Process ◽

Primary Efficacy ◽

Efficacy Data ◽

Assay Optimization ◽

Bioassay Data ◽

Robust Statistical Methods

Well-behaved, in vitro bioassays generally produce normally distributed values in their primary (efficacy) data. Accordingly, the best practices for statistical analysis are well documented and understood. However, assays may occasionally display unusually high variability and fall outside the assumptions inherent in these standard analyses. These assays may still be in the optimization phase, in which the source of variation could be identified and addressed. They might also represent the best available option to address the biological process being examined. In these cases, the use of robust statistical methods may provide a more appropriate set of tools for both data analysis and assay optimization. This article provides guidance on best practices for the use of robust statistical methods for the analysis of bioassay data as an alternative to standard methods. Impacts on experimental design and interpretation will be discussed.

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