Evidence map›Paper›PMID 30057503›Full record

ArticleJournal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience2018

The New Statistics for Neuroscience Majors: Thinking in Effect Sizes.

Robert J Calin-Jageman

Abstract read
In one paragraph

Article in Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Robert J Calin-JagemanPsychology Department, Dominican University, River Forest, IL 60305.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An ongoing reform in statistical practice is to report and interpret effect sizes. This paper provides a short tutorial on effect sizes and some tips on how to help your students think in terms of effect sizes when analyzing data. An effect size is just a quantitative answer to a research question. Effect sizes should always be accompanied by a confidence interval or some other means of expressing uncertainty in generalizing from the sample to the population. Effect sizes are best interpreted in raw scores, but can also be expressed in standardized terms; several popular standardized effect score measures are explained and compared. Reporting and interpreting effect sizes has several benefits: it focuses on the practical significance of your findings, helps make clear the remaining uncertainty in your findings, fosters better planning for subsequent experiments, fosters meta-analytic thinking, and can help focus efforts on protocol optimization. You can help your students start to think in effect sizes by giving them tools to visualize and translate between different effect size measures, and by tasking them to build a 'library' of effect sizes in a research field of interest.

Indexed as

confidence intervalseffect sizesinferential statisticsneuroscience education

Identifiers

PMID30057503
PMCPMC6057753

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.