Evidence map›Paper›PMID 33777409›Full record

ArticleBJPsych advances2021

Measures of variability and precision in statistics: appreciating, untangling and applying concepts.

Adam Ciarleglio

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BJPsych advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Adam Ciarlegliothe Department of Biostatistics and Bioinformatics at the Milken Institute School of Public Health, George Washington University, Washington, DC, USA. His research focuses on developing and applying statistical methods to address mental health research questions.ORCID 0000-0001-7986-3412

Funding

Statistical and Machine Learning Methods for Integrating Clinical and Multimodal Imaging Data to Select Optimal Antidepressant TreatmentK01MH113850 · NIMH · GEORGE WASHINGTON UNIVERSITY · PI CIARLEGLIO, ADAM · 2018 to 2021
$647k
NIMH NIH HHS K01 MH113850
6 · The paper itself

Abstract

This reflection presents a discussion of some common measures of variability and how they are appropriately used in descriptive and inferential statistical analyses. We argue that confidence intervals (CIs), which incorporate these measures, serve as tools to assess both clinical and statistical significance.

Indexed as

confidence intervaldescriptive statisticsinferenceStandard deviationstandard error

Identifiers

PMID33777409
PMCPMC7995880

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.