The Extent and Consequences of P-Hacking in Science
Publication Date
March 13, 2015
Journal
PLOS Biology
Authors
Megan L. Head, Luke Holman, Rob Lanfear, Andrew T. Kahn, et al
Volume
13
Issue
3
Pages
e1002106
DOI
https://dx.plos.org/10.1371/journal.pbio.1002106
Publisher URL
http://journals.plos.org/plosbiology/article?id=10.1371%2Fjournal.pbio.1002106
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25768323
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4359000
Europe PMC
http://europepmc.org/abstract/MED/25768323
Web of Science
000352095700015
Scopus
84926391432
Mendeley
http://www.mendeley.com/research/extent-consequences-phacking-science
Events
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{"title"=>"The Extent and Consequences of P-Hacking in Science", "type"=>"journal", "authors"=>[{"first_name"=>"Megan L.", "last_name"=>"Head", "scopus_author_id"=>"8297849100"}, {"first_name"=>"Luke", "last_name"=>"Holman", "scopus_author_id"=>"23501824500"}, {"first_name"=>"Rob", "last_name"=>"Lanfear", "scopus_author_id"=>"25228756200"}, {"first_name"=>"Andrew T.", "last_name"=>"Kahn", "scopus_author_id"=>"35603737000"}, {"first_name"=>"Michael D.", "last_name"=>"Jennions", "scopus_author_id"=>"7004123448"}], "year"=>2015, "source"=>"PLoS Biology", "identifiers"=>{"pmid"=>"25768323", "issn"=>"15457885", "sgr"=>"84926391432", "pui"=>"603498589", "scopus"=>"2-s2.0-84926391432", "isbn"=>"1545-7885 (Electronic) 1544-9173 (Linking)", "doi"=>"10.1371/journal.pbio.1002106"}, "id"=>"30b4dbaf-f0cb-30fd-b6d2-01ede3fc083a", "abstract"=>"A focus on novel, confirmatory, and statistically significant results leads to substantial bias in the scientific literature. One type of bias, known as “p-hacking,” occurs when researchers collect or select data or statistical analyses until nonsignificant results become significant. Here, we use text-mining to demonstrate that p-hacking is widespread throughout science. We then illustrate how one can test for p-hacking when performing a meta-analysis and show that, while p-hacking is probably common, its effect seems to be weak relative to the real effect sizes beingmeasured. This result suggests that p-hacking probably does not drastically alter scientific consensuses drawn from meta-analyses.", "link"=>"http://www.mendeley.com/research/extent-consequences-phacking-science", "reader_count"=>904, "reader_count_by_academic_status"=>{"Unspecified"=>15, "Professor > Associate Professor"=>43, "Librarian"=>9, "Researcher"=>183, "Student > Doctoral Student"=>36, "Student > Ph. D. 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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1947929"], "description"=>"<p>Number of <i>p</i>-values in each bin is the mean number based on 1,000 bootstraps of one <i>p</i>-value per Results section, rounded to the nearest whole number. Disciplines (n = 8) for which we found fewer than 50 <i>p</i>-values below 0.05 in the Results section were excluded.</p><p>Tests for evidential value and p-hacking across disciplines, using <i>p</i>-values obtained from the Results section.</p>", "links"=>[], "tags"=>["nonsignificant results", "bias", "effect sizes"], "article_id"=>1335038, "categories"=>["Biological Sciences"], "users"=>["Megan L. Head", "Luke Holman", "Rob Lanfear", "Andrew T. Kahn", "Michael D. Jennions"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002106.t001", "stats"=>{"downloads"=>4, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Tests_for_evidential_value_and_p_hacking_across_disciplines_using_p_values_obtained_from_the_Results_section_/1335038", "title"=>"Tests for evidential value and p-hacking across disciplines, using <i>p</i>-values obtained from the Results section.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-13 03:47:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/1947926"], "description"=>"<p>A) Evidence for p-hacking from <i>p</i>-values obtained from Results sections. B) Evidence for p-hacking from <i>p</i>-values obtained from Abstracts. The strength of p-hacking is presented as the proportion of p-values in the upper bin (0.045 < p < 0.05) with one-tailed 95% confidence intervals (calculated following Clopper and Pearson [<a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002106#pbio.1002106.ref047\" target=\"_blank\">47</a>] using the <i>binom.test</i> function in R). Only disciplines where text-mining of the Results sections returned more than 25 <i>p</i>-values between 0.04 and 0.05 are presented. Marker colour is shaded according to the sample size: with white indicating low samples sizes and red indicating larger sample sizes.</p>", "links"=>[], "tags"=>["nonsignificant results", "bias", "effect sizes"], "article_id"=>1335035, "categories"=>["Biological Sciences"], "users"=>["Megan L. Head", "Luke Holman", "Rob Lanfear", "Andrew T. Kahn", "Michael D. Jennions"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002106.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evidence_for_p_hacking_across_scientific_disciplines_/1335035", "title"=>"Evidence for p-hacking across scientific disciplines.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-13 03:47:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/1947925"], "description"=>"<p>A) Black line shows distribution of <i>p</i>-values when there is no evidential value and the red line shows how p-hacking influences this distribution. B) Black line shows distribution of <i>p</i>-values when there is evidential value and the red line shows how p-hacking influences this distribution. Tests for p-hacking often compare the number of <i>p</i>-values in two adjacent bins just below 0.05.</p>", "links"=>[], "tags"=>["nonsignificant results", "bias", "effect sizes"], "article_id"=>1335034, "categories"=>["Biological Sciences"], "users"=>["Megan L. Head", "Luke Holman", "Rob Lanfear", "Andrew T. Kahn", "Michael D. Jennions"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002106.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effect_of_p_hacking_on_the_distribution_of_p_values_in_the_range_of_significance_/1335034", "title"=>"The effect of p-hacking on the distribution of p-values in the range of significance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-13 03:47:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/1947933"], "description"=>"<div><p>A focus on novel, confirmatory, and statistically significant results leads to substantial bias in the scientific literature. One type of bias, known as “p-hacking,” occurs when researchers collect or select data or statistical analyses until nonsignificant results become significant. Here, we use text-mining to demonstrate that p-hacking is widespread throughout science. We then illustrate how one can test for p-hacking when performing a meta-analysis and show that, while p-hacking is probably common, its effect seems to be weak relative to the real effect sizes being measured. This result suggests that p-hacking probably does not drastically alter scientific consensuses drawn from meta-analyses.</p></div>", "links"=>[], "tags"=>["nonsignificant results", "bias", "effect sizes"], "article_id"=>1335042, "categories"=>["Biological Sciences"], "users"=>["Megan L. Head", "Luke Holman", "Rob Lanfear", "Andrew T. Kahn", "Michael D. Jennions"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002106", "stats"=>{"downloads"=>27, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Extent_and_Consequences_of_P_Hacking_in_Science_/1335042", "title"=>"The Extent and Consequences of P-Hacking in Science", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-13 03:47:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/1947928"], "description"=>"<p>The p-curve shows evidence for evidential value (strong right skew) and p-hacking (rise in <i>p</i>-values just below 0.05).</p>", "links"=>[], "tags"=>["nonsignificant results", "bias", "effect sizes"], "article_id"=>1335037, "categories"=>["Biological Sciences"], "users"=>["Megan L. Head", "Luke Holman", "Rob Lanfear", "Andrew T. Kahn", "Michael D. Jennions"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002106.g004", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_distribution_of_p_values_associated_with_the_meta_analysis_conducted_by_Jiang_et_al_2013_/1335037", "title"=>"The distribution of <i>p</i>-values associated with the meta-analysis conducted by Jiang et al. (2013).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-13 03:47:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/1947915"], "description"=>"<p>A) Black line shows distribution of p-values when there is no evidential value and the red line shows how publication bias influences this distribution. B) Black line shows distribution of <i>p</i>-values when there is evidential value and red line shows how publication bias influences this distribution. Tests for publication bias due to a file-drawer effect often compare the number of <i>p</i>-values in each of the bins either side of 0.05.</p>", "links"=>[], "tags"=>["nonsignificant results", "bias", "effect sizes"], "article_id"=>1335032, "categories"=>["Biological Sciences"], "users"=>["Megan L. Head", "Luke Holman", "Rob Lanfear", "Andrew T. Kahn", "Michael D. Jennions"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002106.g001", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effect_of_publication_bias_on_the_distribution_of_p_values_around_the_significance_threshold_of_0_05_/1335032", "title"=>"The effect of publication bias on the distribution of <i>p</i>-values around the significance threshold of 0.05.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-13 03:47:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/1947931"], "description"=>"<p>Meta-analyses with ten or fewer significant <i>p</i>-values are not shown. Numbers in the lower and upper bins of the p-hacking test are those including misreported <i>p</i>-values followed by those excluding misreported <i>p</i>-values in brackets.</p><p>Tests for evidential value and p-hacking for published meta-analyses.</p>", "links"=>[], "tags"=>["nonsignificant results", "bias", "effect sizes"], "article_id"=>1335041, "categories"=>["Biological Sciences"], "users"=>["Megan L. Head", "Luke Holman", "Rob Lanfear", "Andrew T. Kahn", "Michael D. Jennions"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002106.t003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Tests_for_evidential_value_and_p_hacking_for_published_meta_analyses_/1335041", "title"=>"Tests for evidential value and p-hacking for published meta-analyses.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-13 03:47:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/1947930"], "description"=>"<p>Number of <i>p</i>-values in each bin is the mean number based on 1,000 bootstraps of one <i>p</i>-value per Abstract, rounded to the nearest whole number. Disciplines (n = 12) for which we found fewer than 50 <i>p</i>-values below 0.05 in the Abstract were excluded.</p><p>Tests for evidential value and p-hacking across disciplines, using <i>p</i>-values obtained from the Abstract.</p>", "links"=>[], "tags"=>["nonsignificant results", "bias", "effect sizes"], "article_id"=>1335039, "categories"=>["Biological Sciences"], "users"=>["Megan L. Head", "Luke Holman", "Rob Lanfear", "Andrew T. Kahn", "Michael D. Jennions"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002106.t002", "stats"=>{"downloads"=>5, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Tests_for_evidential_value_and_p_hacking_across_disciplines_using_p_values_obtained_from_the_Abstract_/1335039", "title"=>"Tests for evidential value and p-hacking across disciplines, using <i>p</i>-values obtained from the Abstract.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-13 03:47:55"}

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  • {"unique-ip"=>"153", "full-text"=>"168", "pdf"=>"11", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"10", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"159", "full-text"=>"185", "pdf"=>"13", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}
  • {"unique-ip"=>"234", "full-text"=>"308", "pdf"=>"14", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"8", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}
  • {"unique-ip"=>"233", "full-text"=>"268", "pdf"=>"18", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2020", "month"=>"11"}
  • {"unique-ip"=>"154", "full-text"=>"193", "pdf"=>"14", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2020", "month"=>"12"}
  • {"unique-ip"=>"136", "full-text"=>"172", "pdf"=>"25", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2021", "month"=>"1"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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