Evidence map›Paper›PMID 27199532›Full record

ArticleShanghai archives of psychiatry2015

The debate about p-values.

Ying Lu, Ilana Belitskaya-Levy

Abstract read
In one paragraph

Article in Shanghai archives of psychiatry, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Multiplicity Eludes Peer Review: The Case of COVID-19 Research.International journal of environmental research and public health · 2021
    Observational
  5. 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

2 authors.

Ying LuVA (Veterans Affairs) Cooperative Studies Program Palo Alto Coordinating Center, VA Palo Alto Health Care System, Palo Alto, CA, USA.
Ilana Belitskaya-LevyDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The p-value is the most widely used statistical concept in biomedical research. Recently, there are controversies over its utility and over the possible relationship between p-value misuse and the relatively high proportion of published medical research that cannot be replicated. In this paper, we introduce the p-value in layman's terms and explain its randomness and limitations. However, we also point out that the available alternatives to p-value suffer similar limitations. We conclude that using p values is a valid way to test the null and alternative hypotheses in clinical trials. However, using the p-value from a single statistical test to judge the scientific merit of a research project is a misuse of the p-value; the results of inference tests using p-values need to be integrated with secondary results and other data to arrive at clinically valid conclusions. Understanding the variability and limitations of the p-value is important for the interpretation of statistical results in research studies.

Indexed as

hypothesis testinginferential statisticsp-valuescientific repeatabilitystatistical significance

Identifiers

PMID27199532
PMCPMC4858512

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.