Evidence map›Paper›PMID 34845889›Full record

ArticleJournal of postgraduate medicine

The importance of small samples in medical research.

A Indrayan, A Mishra

Abstract read
In one paragraph

Article in Journal of postgraduate medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 6 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

49 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Trial
  8. Trial
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  17. Reasons for Participating in the EDS-HEART Program: Holistic and Performative Within a Supportive Community.International journal of environmental research and public health · 2025
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  19. Authors' response.The Indian journal of medical research · 2025
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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

2 authors.

A IndrayanClinical Research Department, Max Healthcare, New Delhi, India.
A MishraClinical Research Department, Max Healthcare, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Almost all bio-statisticians and medical researchers believe that a large sample is always helpful in providing more reliable results. Whereas this is true for some specific cases, a large sample may not be helpful in more situations than we contemplate because of the higher possibility of errors and reduced validity. Many medical breakthroughs have occurred with self-experimentation and single experiments. Studies, particularly analytical studies, may provide more truthful results with a small sample because intensive efforts can be made to control all the confounders, wherever they operate, and sophisticated equipment can be used to obtain more accurate data. A large sample may be required only for the studies with highly variable outcomes, where an estimate of the effect size with high precision is required, or when the effect size to be detected is small. This communication underscores the importance of small samples in reaching a valid conclusion in certain situations and describes the situations where a large sample is not only unnecessary but may even compromise the validity by not being able to exercise full care in the assessments. What sample size is small depends on the context.

Indexed as

Biomedical ResearchHumansSample SizeMedical researchn= 1 self-experimentssmall sample

Identifiers

PMID34845889
PMCPMC8706541

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.