Dataset Information

Estimating statistical power, posterior probability and publication bias of psychological research using the observed replication rate.

ABSTRACT: In this paper, we show how Bayes' theorem can be used to better understand the implications of the 36% reproducibility rate of published psychological findings reported by the Open Science Collaboration. We demonstrate a method to assess publication bias and show that the observed reproducibility rate was not consistent with an unbiased literature. We estimate a plausible range for the prior probability of this body of research, suggesting expected statistical power in the original studies of 48-75%, producing (positive) findings that were expected to be true 41-62% of the time. Publication bias was large, assuming a literature with 90% positive findings, indicating that negative evidence was expected to have been observed 55-98 times before one negative result was published. These findings imply that even when studied associations are truly NULL, we expect the literature to be dominated by statistically significant findings.

SUBMITTER: Ingre M

PROVIDER: S-EPMC6170554 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Estimating statistical power, posterior probability and publication bias of psychological research using the observed replication rate.

Ingre Michael M Nilsonne Gustav G

Royal Society open science 20180912 9

In this paper, we show how Bayes' theorem can be used to better understand the implications of the 36% reproducibility rate of published psychological findings reported by the Open Science Collaboration. We demonstrate a method to assess publication bias and show that the observed reproducibility rate was not consistent with an unbiased literature. We estimate a plausible range for the prior probability of this body of research, suggesting expected statistical power in the original studies of 48 ...[more]

PMID: 30839704

Similar Datasets

Project description:Background:Publication bias is a form of scientific misconduct. It threatens the validity of research results and the credibility of science. Although several tests on publication bias exist, no in-depth evaluations are available that examine which test performs best for different research settings. Methods:Four tests on publication bias, Egger's test (FAT), p-uniform, the test of excess significance (TES), as well as the caliper test, were evaluated in a Monte Carlo simulation. Two different types of publication bias and its degree (0%, 50%, 100%) were simulated. The type of publication bias was defined either as file-drawer, meaning the repeated analysis of new datasets, or p-hacking, meaning the inclusion of covariates in order to obtain a significant result. In addition, the underlying effect (β = 0, 0.5, 1, 1.5), effect heterogeneity, the number of observations in the simulated primary studies (N = 100, 500), and the number of observations for the publication bias tests (K = 100, 1,000) were varied. Results:All tests evaluated were able to identify publication bias both in the file-drawer and p-hacking condition. The false positive rates were, with the exception of the 15%- and 20%-caliper test, unbiased. The FAT had the largest statistical power in the file-drawer conditions, whereas under p-hacking the TES was, except under effect heterogeneity, slightly better. The CTs were, however, inferior to the other tests under effect homogeneity and had a decent statistical power only in conditions with 1,000 primary studies. Discussion:The FAT is recommended as a test for publication bias in standard meta-analyses with no or only small effect heterogeneity. If two-sided publication bias is suspected as well as under p-hacking the TES is the first alternative to the FAT. The 5%-caliper test is recommended under conditions of effect heterogeneity and a large number of primary studies, which may be found if publication bias is examined in a discipline-wide setting when primary studies cover different research problems.

Project description:Field studies are essential to reliably quantify ecological responses to global change because they are exposed to realistic climate manipulations. Yet such studies are limited in replicates, resulting in less power and, therefore, potentially unreliable effect estimates. Furthermore, while manipulative field experiments are assumed to be more powerful than non-manipulative observations, it has rarely been scrutinized using extensive data. Here, using 3847 field experiments that were designed to estimate the effect of environmental stressors on ecosystems, we systematically quantified their statistical power and magnitude (Type M) and sign (Type S) errors. Our investigations focused upon the reliability of field experiments to assess the effect of stressors on both ecosystem's response magnitude and variability. When controlling for publication bias, single experiments were underpowered to detect response magnitude (median power: 18%-38% depending on effect sizes). Single experiments also had much lower power to detect response variability (6%-12% depending on effect sizes) than response magnitude. Such underpowered studies could exaggerate estimates of response magnitude by 2-3 times (Type M errors) and variability by 4-10 times. Type S errors were comparatively rare. These observations indicate that low power, coupled with publication bias, inflates the estimates of anthropogenic impacts. Importantly, we found that meta-analyses largely mitigated the issues of low power and exaggerated effect size estimates. Rather surprisingly, manipulative experiments and non-manipulative observations had very similar results in terms of their power, Type M and S errors. Therefore, the previous assumption about the superiority of manipulative experiments in terms of power is overstated. These results call for highly powered field studies to reliably inform theory building and policymaking, via more collaboration and team science, and large-scale ecosystem facilities. Future studies also require transparent reporting and open science practices to approach reproducible and reliable empirical work and evidence synthesis.

Project description:Collaborative efforts to directly replicate empirical studies in the medical and social sciences have revealed alarmingly low rates of replicability, a phenomenon dubbed the 'replication crisis'. Poor replicability has spurred cultural changes targeted at improving reliability in these disciplines. Given the absence of equivalent replication projects in ecology and evolutionary biology, two inter-related indicators offer the opportunity to retrospectively assess replicability: publication bias and statistical power. This registered report assesses the prevalence and severity of small-study (i.e., smaller studies reporting larger effect sizes) and decline effects (i.e., effect sizes decreasing over time) across ecology and evolutionary biology using 87 meta-analyses comprising 4,250 primary studies and 17,638 effect sizes. Further, we estimate how publication bias might distort the estimation of effect sizes, statistical power, and errors in magnitude (Type M or exaggeration ratio) and sign (Type S). We show strong evidence for the pervasiveness of both small-study and decline effects in ecology and evolution. There was widespread prevalence of publication bias that resulted in meta-analytic means being over-estimated by (at least) 0.12 standard deviations. The prevalence of publication bias distorted confidence in meta-analytic results, with 66% of initially statistically significant meta-analytic means becoming non-significant after correcting for publication bias. Ecological and evolutionary studies consistently had low statistical power (15%) with a 4-fold exaggeration of effects on average (Type M error rates = 4.4). Notably, publication bias reduced power from 23% to 15% and increased type M error rates from 2.7 to 4.4 because it creates a non-random sample of effect size evidence. The sign errors of effect sizes (Type S error) increased from 5% to 8% because of publication bias. Our research provides clear evidence that many published ecological and evolutionary findings are inflated. Our results highlight the importance of designing high-power empirical studies (e.g., via collaborative team science), promoting and encouraging replication studies, testing and correcting for publication bias in meta-analyses, and adopting open and transparent research practices, such as (pre)registration, data- and code-sharing, and transparent reporting.

Dataset Information

Estimating statistical power, posterior probability and publication bias of psychological research using the observed replication rate.

Publications

Estimating statistical power, posterior probability and publication bias of psychological research using the observed replication rate.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets