PubMed Health⌕ Search

PubMed · 1936103

Statistical tools and status symbols.

Abstract

This paper sets the stage for a series of edited papers which follow. It is argued that obvious statistical blunders (mortal sins) have not disappeared entirely from the aging literature, but that the most frequent error now is that of choosing a less powerful analytic solution when a more powerful (and equally applicable) one is at hand (a venial sin). Clearly, it is argued, power must be considered in relationship to the theoretical model, the condition of the data set, and the research question. Editors and reviewers, it is argued, must cut through the "glitter" of new fads and the security of tradition in order to objectively consider the merits of new statistical applications. More importantly, without knowledge of the issues, limitations, and advantages of contemporary and emerging analytic techniques, authors cannot make good analytic choices and defend them effectively. The time between presentation of innovative approaches in the quantitative journals and application in the aging literature contributes to traditionalism in data analysis. Overzealousness by advocates of new approaches often contributes to premature embracing of them. Thus the papers that follow were designed to bring new methods to the "working-researcher" with a frank appraisal of assumptions, proper and improper applications, and limitations. Authors of these papers were challenged to communicate in plain and understandable terms and to provide practical examples.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M F Elias. 1991. Statistical tools and status symbols.. https://doi.org/10.1080/03610739108253881

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans↗