Evidence map›Paper›PMID 25213136›Full record

ArticleNaunyn-Schmiedeberg's archives of pharmacology2014

Common misconceptions about data analysis and statistics.

Harvey J Motulsky

Abstract read
In one paragraph

Article in Naunyn-Schmiedeberg's archives of pharmacology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

  1. The negative chronotropic effects of (±)-propranolol and (±)-4-NONaunyn-Schmiedeberg's archives of pharmacology · 2025
    Article
  2. Review
  3. The roles, challenges, and merits of the p value.Patterns (New York, N.Y.) · 2023
    Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. βNaunyn-Schmiedeberg's archives of pharmacology · 2022
    Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Observational
  15. Review
  16. The Use ofFrontiers in veterinary science · 2018
    Review
  17. KNaunyn-Schmiedeberg's archives of pharmacology · 2017
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Harvey J MotulskyGraphPad Software Inc., La Jolla, California, USA, hmotulsky@graphpad.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ideally, any experienced investigator with the right tools should be able to reproduce a finding published in a peer-reviewed biomedical science journal. In fact, the reproducibility of a large percentage of published findings has been questioned. Undoubtedly, there are many reasons for this, but one reason maybe that investigators fool themselves due to a poor understanding of statistical concepts. In particular, investigators often make these mistakes: 1. P-Hacking. This is when you reanalyze a data set in many different ways, or perhaps reanalyze with additional replicates, until you get the result you want. 2. Overemphasis on P values rather than on the actual size of the observed effect. 3. Overuse of statistical hypothesis testing, and being seduced by the word "significant". 4. Overreliance on standard errors, which are often misunderstood.

Indexed as

Data Interpretation, StatisticalHumansPeer Review, ResearchPeriodicals as TopicReproducibility of Results

Identifiers

PMID25213136
PMCPMC4203998

What OpenQuestion holds

Textmetadata
LicenceCC BY-ND
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.