Evidence map›Paper›PMID 33414750›Full record

ArticleFrontiers in psychology2020

Statistical Significance Filtering Overestimates Effects and Impedes Falsification: A Critique of.

Jonathan Z Bakdash, Laura R Marusich, Jared B Kenworthy, Elyssa Twedt, Erin G Zaroukian

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

  1. Pooled it
  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

5 authors.

Jonathan Z BakdashUnited States Army Combat Capabilities Development Command, Army Research Laboratory South at the University of Texas at Dallas, Richardson, TX, United States.
Laura R MarusichUnited States Army Combat Capabilities Development Command, Army Research Laboratory South at the University of Texas at Arlington, Arlington, TX, United States.
Jared B KenworthyDepartment of Psychology, University of Texas at Arlington, Arlington, TX, United States.
Elyssa TwedtDepartment of Psychology, St. Lawrence University, Canton, NY, United States.
Erin G ZaroukianUnited States Army Combat Capabilities Development Command, Army Research Laboratory, Computational and Information Sciences Directorate, Aberdeen, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Whether in meta-analysis or single experiments, selecting results based on statistical significance leads to overestimated effect sizes, impeding falsification. We critique a quantitative synthesis that used significance to score and select previously published effects for situation awareness-performance associations (Endsley, 2019). How much does selection using statistical significance quantitatively impact results in a meta-analytic context? We evaluate and compare results using significance-filtered effects versus analyses with all effects as-reported. Endsley reported high predictiveness scores and large positive mean correlations but used atypical methods: the hypothesis was used to select papers and effects. Papers were assigned the maximum predictiveness scores if they contained

Indexed as

confirmation biasfalsificationmeta-analysisperformancep-hackingselection biassignificance filtersituation awareness

Identifiers

PMID33414750
PMCPMC7783317

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.