Journal article Open Access

<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <identifier identifierType="URL">https://zenodo.org/record/235811</identifier> <creators> <creator> <creatorName>Lakens, Daniël</creatorName> <givenName>Daniël</givenName> <familyName>Lakens</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0247-239X</nameIdentifier> </creator> </creators> <titles> <title>What p -hacking really looks like: A comment on Masicampo and LaLande (2012)</title> </titles> <publisher>Zenodo</publisher> <publicationYear>2014</publicationYear> <dates> <date dateType="Issued">2014-12-06</date> </dates> <resourceType resourceTypeGeneral="JournalArticle"/> <alternateIdentifiers> <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/235811</alternateIdentifier> </alternateIdentifiers> <relatedIdentifiers> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1080/17470218.2014.982664</relatedIdentifier> </relatedIdentifiers> <rightsList> <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights> </rightsList> <descriptions> <description descriptionType="Abstract">Masicampo and Lalande (2012; M&amp;L) assessed the distribution of 3627 exactly calculated p-values between 0.01 and 0.10 from 12 issues of three journals. The authors concluded that "The number of p-values in the psychology literature that barely meet the criterion for statistical significance (i.e., that fall just below .05) is unusually large". "Specifically, the number of p-values between .045 and .050 was higher than that predicted based on the overall distribution of p." There are four factors that determine the distribution of p-values, namely the number of studies examining true effect and false effects, the power of the studies that examine true effects, the frequency of Type 1 error rates (and how they were inflated), and publication bias. Due to publication bias, we should expect a substantial drop in the frequency with which p-values above .05 appear in the literature. True effects yield a right-skewed p-curve (the higher the power, the steeper the curve, e.g., Sellke, Bayarri, &amp; Berger, 2001). When the null-hypothesis is true the p-curve is uniformly distributed, but when the Type 1 error rate is inflated due to flexibility in the data-analysis, the p-curve could become left-skewed below pvalues of .05. M&amp;L (and others, e.g., Leggett, Thomas, Loetscher, &amp; Nicholls, 2013) model pvalues based on a single exponential curve estimation procedure that provides the best fit of p-values between .01 and .10 (see Figure 3, right pane). This is not a valid approach because p-values above and below p=.05 do not lie on a continuous curve due to publication bias. It is therefore not surprising, nor indicative of a prevalence of p-values just below .05, that their single curve doesn't fit the data very well, nor that Chi-squared tests show the residuals (especially those just below .05) are not randomly distributed.</description> </descriptions> </resource>

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