Publication bias and the canonization of false facts

Research output: Contribution to journal › Journal article › Research › peer-review

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Publication bias and the canonization of false facts. / Nissen, Silas Boye; Magidson, Tali; Gross, Kevin; Bergstrom, Carl T.

In: eLife, Vol. 5, e21451, 20.12.2016, p. 1-19.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Nissen, SB, Magidson, T, Gross, K & Bergstrom, CT 2016, 'Publication bias and the canonization of false facts', eLife, vol. 5, e21451, pp. 1-19. https://doi.org/10.7554/eLife.21451.001

APA

Nissen, S. B., Magidson, T., Gross, K., & Bergstrom, C. T. (2016). Publication bias and the canonization of false facts. eLife, 5, 1-19. [e21451]. https://doi.org/10.7554/eLife.21451.001

Vancouver

Nissen SB, Magidson T, Gross K, Bergstrom CT. Publication bias and the canonization of false facts. eLife. 2016 Dec 20;5:1-19. e21451. https://doi.org/10.7554/eLife.21451.001

Author

Nissen, Silas Boye ; Magidson, Tali ; Gross, Kevin ; Bergstrom, Carl T. / Publication bias and the canonization of false facts. In: eLife. 2016 ; Vol. 5. pp. 1-19.

Bibtex

@article{0376c3a5756b4ad2a7e5f79f00e8f6df,
title = "Publication bias and the canonization of false facts",
abstract = "Science is facing a {"}replication crisis{"} in which many experimental findings cannot be replicated and are likely to be false. Does this imply that many scientific facts are false as well? To find out, we explore the process by which a claim becomes fact. We model the community's confidence in a claim as a Markov process with successive published results shifting the degree of belief. Publication bias in favor of positive findings influences the distribution of published results. We find that unless a sufficient fraction of negative results are published, false claims frequently can become canonized as fact. Data-dredging, p-hacking, and similar behaviors exacerbate the problem. Should negative results become easier to publish as a claim approaches acceptance as a fact, however, true and false claims would be more readily distinguished. To the degree that the model reflects the real world, there may be serious concerns about the validity of purported facts in some disciplines.",
author = "Nissen, {Silas Boye} and Tali Magidson and Kevin Gross and Bergstrom, {Carl T}",
year = "2016",
month = dec,
day = "20",
doi = "10.7554/eLife.21451.001",
language = "English",
volume = "5",
pages = "1--19",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications Ltd.",

}

RIS

TY - JOUR

T1 - Publication bias and the canonization of false facts

AU - Nissen, Silas Boye

AU - Magidson, Tali

AU - Gross, Kevin

AU - Bergstrom, Carl T

PY - 2016/12/20

Y1 - 2016/12/20

N2 - Science is facing a "replication crisis" in which many experimental findings cannot be replicated and are likely to be false. Does this imply that many scientific facts are false as well? To find out, we explore the process by which a claim becomes fact. We model the community's confidence in a claim as a Markov process with successive published results shifting the degree of belief. Publication bias in favor of positive findings influences the distribution of published results. We find that unless a sufficient fraction of negative results are published, false claims frequently can become canonized as fact. Data-dredging, p-hacking, and similar behaviors exacerbate the problem. Should negative results become easier to publish as a claim approaches acceptance as a fact, however, true and false claims would be more readily distinguished. To the degree that the model reflects the real world, there may be serious concerns about the validity of purported facts in some disciplines.

AB - Science is facing a "replication crisis" in which many experimental findings cannot be replicated and are likely to be false. Does this imply that many scientific facts are false as well? To find out, we explore the process by which a claim becomes fact. We model the community's confidence in a claim as a Markov process with successive published results shifting the degree of belief. Publication bias in favor of positive findings influences the distribution of published results. We find that unless a sufficient fraction of negative results are published, false claims frequently can become canonized as fact. Data-dredging, p-hacking, and similar behaviors exacerbate the problem. Should negative results become easier to publish as a claim approaches acceptance as a fact, however, true and false claims would be more readily distinguished. To the degree that the model reflects the real world, there may be serious concerns about the validity of purported facts in some disciplines.

U2 - 10.7554/eLife.21451.001

DO - 10.7554/eLife.21451.001

M3 - Journal article

C2 - 27995896

VL - 5

SP - 1

EP - 19

JO - eLife

JF - eLife

SN - 2050-084X

M1 - e21451

ER -

ID: 170343523