Evidence map›Paper›PMID 33443615›Full record

ArticleNaunyn-Schmiedeberg's archives of pharmacology2021

A teaching tool about the fickle p value and other statistical principles based on real-life data.

Salem Alawbathani, Mehreen Batool, Jan Fleckhaus, Sarkawt Hamad, Floyd Hassenrück, Yanhong Hou, Xia Li, Jon Salmanton-García, Sami Ullah, Frederique Wieters and 1 more

Abstract read
In one paragraph

Article in Naunyn-Schmiedeberg's archives of pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Salem AlawbathaniCenter for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.
Mehreen BatoolCenter for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.
Jan FleckhausCenter for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.
Sarkawt HamadCenter for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.ORCID 0000-0002-5155-9467
Floyd HassenrückCenter for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.ORCID 0000-0003-4772-5220
Yanhong HouCenter for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.ORCID 0000-0003-2248-8441
Xia LiCenter for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.ORCID 0000-0001-7765-0333
Jon Salmanton-GarcíaCenter for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.ORCID 0000-0002-6766-8297
Sami UllahCenter for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.
Frederique WietersCenter for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.
Martin C MichelDept. of Pharmacology, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany. marmiche@uni-mainz.de.ORCID 0000-0003-4161-8467

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A poor understanding of statistical analysis has been proposed as a key reason for lack of replicability of many studies in experimental biomedicine. While several authors have demonstrated the fickleness of calculated p values based on simulations, we have experienced that such simulations are difficult to understand for many biomedical scientists and often do not lead to a sound understanding of the role of variability between random samples in statistical analysis. Therefore, we as trainees and trainers in a course of statistics for biomedical scientists have used real data from a large published study to develop a tool that allows scientists to directly experience the fickleness of p values. A tool based on a commonly used software package was developed that allows using random samples from real data. The tool is described and together with the underlying database is made available. The tool has been tested successfully in multiple other groups of biomedical scientists. It can also let trainees experience the impact of randomness, sample sizes and choice of specific statistical test on measured p values. We propose that live exercises based on real data will be more impactful in the training of biomedical scientists on statistical concepts.

Indexed as

Data Interpretation, StatisticalResearch DesignBiomedical ResearchComputer SimulationHumansReproducibility of ResultsSample SizeSoftwareStatistics as TopicTeachingP valueReplicabilityStatistical analysisTeaching

Identifiers

PMID33443615
PMCPMC8208928

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.