Research graph
References from Finding the right power balance: Better study design and collaboration can reduce dependence on statistical power. Local targets link to admitted publications; unresolved targets remain external evidence.
Avoidable waste in the production and reporting of research evidence
10.1016/s0140-6736(09)60329-9 · 2009 · External reference
Quantifying research waste in ecology
10.1038/s41559-022-01820-0 · 2022 · External reference
Why most published research findings are false.
2005 · External reference
Scientists rise up against statistical significance
10.1038/d41586-019-00857-9 · 2019 · External reference
Moving to a World Beyond "p < 0.05".
10.1080/00031305.2019.1583913 · 2019 · External reference
Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling.
10.1177/0956797611430953 · 2012 · External reference
Questionable research practices in ecology and evolution
10.1371/journal.pone.0200303 · 2018 · External reference
1,500 scientists lift the lid on reproducibility
10.1038/533452a · 2016 · External reference
Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication.
10.1080/00031305.2018.1543137 · 2019 · External reference
Analysis goals, error-cost sensitivity, and analysis hacking: Essential considerations in hypothesis testing and multiple comparisons
10.1111/ppe.12711 · 2021 · External reference
Effect size, confidence interval and statistical significance: a practical guide for biologists
10.1111/j.1469-185x.2007.00027.x · 2007 · External reference
Null hypothesis significance testing: A review of an old and continuing controversy.
10.1037/1082-989x.5.2.241 · 2000 · External reference
The Fallacy of the Null-Hypothesis Significance Test.
10.1037/h0042040 · 1960 · External reference
Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors.
10.1177/1745691614551642 · 2014 · External reference
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
10.1007/s10654-016-0149-3 · 2016 · External reference
Planning Study Size Based on Precision Rather Than Power.
10.1097/ede.0000000000000876 · 2018 · External reference
Confidence and precision increase with high statistical power.
10.1038/nrn3475-c4 · 2013 · External reference
The overemphasis on power analysis.
10.1097/00006199-199611000-00018 · 1996 · External reference
The continuing unethical conduct of underpowered clinical trials
10.1001/jama.288.3.358 · 2002 · External reference
Are sample sizes of randomized clinical trials in rheumatoid arthritis too large?
10.1111/eci.12337 · 2014 · External reference
Unresolved reference
1988 · External reference
Type S error rates for classical and Bayesian single and multiple comparison procedures.
10.1007/s001800000040 · 2000 · External reference
Addressing exaggeration of effects from single RCTs.
10.1111/1740-9713.01587 · 2021 · External reference
16 S-Squared over D-Squared—a Relation for Crude Sample-Size Estimates.
10.1002/sim.4780110811 · 1992 · External reference
Obtaining evidence by a single well-powered trial or several modestly powered trials.
10.1177/0962280212461098 · 2016 · External reference
Reproducibility of preclinical animal research improves with heterogeneity of study samples
10.1371/journal.pbio.2003693 · 2018 · External reference
Estimating the reproducibility of psychological science
2015 · External reference
Evaluating replicability of laboratory experiments in economics
10.1126/science.aaf0918 · 2016 · External reference
Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015.
10.1038/s41562-018-0399-z · 2018 · External reference
Investigating the replicability of preclinical cancer biology
2021 · External reference
The Economics of Reproducibility in Preclinical Research
10.1371/journal.pbio.1002165 · 2015 · External reference
Comparing meta-analyses and preregistered multiple-laboratory replication projects.
10.1038/s41562-019-0787-z · 2020 · External reference
Power failure: why small sample size undermines the reliability of neuroscience
10.1038/nrn3475 · 2013 · External reference
Statistical power of clinical trials increased while effect size remained stable: an empirical analysis of 136,212 clinical trials between 1975 and 2014.
10.1016/j.jclinepi.2018.06.014 · 2018 · External reference
The Power of Bias in Economics Research
10.1111/ecoj.12461 · 2017 · External reference
What Meta-Analyses Reveal About the Replicability of Psychological Research.
10.1037/bul0000169 · 2018 · External reference
Low statistical power and overestimated anthropogenic impacts, exacerbated by publication bias, dominate field studies in global change biology
10.1111/gcb.15972 · 2022 · External reference
Publication bias impacts on effect size, statistical power, and magnitude (Type M) and sign (Type S) errors in ecology and evolutionary biology.
2023 · External reference
Empirical evidence of widespread exaggeration bias and selective reporting in ecology
2023 · External reference
Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies.
10.11613/bm.2021.010502 · 2021 · External reference
Science is not a signal detection problem
10.1073/pnas.1914237117 · 2020 · External reference
Cryptic multiple hypotheses testing in linear models: overestimated effect sizes and the winner’s curse
10.1007/s00265-010-1038-5 · 2011 · External reference
Statistical correction of the Winner’s Curse explains replication variability in quantitative trait genome-wide association studies.
10.1371/journal.pgen.1006916 · 2017 · External reference
The intriguing evolution of effect sizes in biomedical research over time: smaller but more often statistically significant.
10.1093/gigascience/gix121 · 2017 · External reference
Unresolved reference
2012 · External reference
Candidate and non-candidate genes in behavior genetics
10.1016/j.conb.2012.07.005 · 2013 · External reference
Statistical simulations show that scientists need not increase overall sample size by default when including both sexes in in vivo studies
10.1371/journal.pbio.3002129 · 2023 · External reference
10.1017/9781139161879
10.1017/9781139161879 · 2020 · External reference
Agrochemicals interact synergistically to increase bee mortality
10.1038/s41586-021-03787-7 · 2021 · External reference
Performing high-powered studies efficiently with sequential analyses
10.1002/ejsp.2023 · 2014 · External reference
Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses
10.1177/1948550617697177 · 2017 · External reference
An introduction to power and sample size estimation.
10.1136/emj.20.5.453 · 2003 · External reference
Small sample size problems in wildlife ecology: a contingent analytical approach
10.2981/wlb.1999.010 · 1999 · External reference
Reproducibility of animal research in light of biological variation.
10.1038/s41583-020-0313-3 · 2020 · External reference
Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research
10.1371/journal.pbio.3001009 · 2021 · External reference
Reproducibility Crisis: Are We Ignoring Reaction Norms?
10.1016/j.tips.2016.05.003 · 2016 · External reference
Mapping phenotypes: canalization, plasticity and developmental stability
10.1016/s0169-5347(01)02266-2 · 2001 · External reference
Reproducible preclinical research-Is embracing variability the answer?
2018 · External reference
The standardization-generalization dilemma: a way out
10.1111/j.1601-183x.2010.00628.x · 2010 · External reference
Reply to ’It is time for an empirically informed paradigm shift in animal research’.
10.1038/s41583-020-0370-7 · 2020 · External reference
Systematic variation improves reproducibility of animal experiments.
10.1038/nmeth0310-167 · 2010 · External reference
Environmental standardization: cure or cause of poor reproducibility in animal experiments?
10.1038/nmeth.1312 · 2009 · External reference
On the importance of systems thinking when using the ALARP principle for risk management.
10.1016/j.ress.2020.107222 · 2020 · External reference
Unresolved reference
External reference
10.1002/0470074353
10.1002/0470074353 · 2007 · External reference
10.1007/978-3-030-03499-3
10.1007/978-3-030-03499-3 · 2019 · External reference
A Bayesian predictive approach for dealing with pseudoreplication.
2020 · External reference
Using Biological Insight and Pragmatism When Thinking about Pseudoreplication
10.1016/j.tree.2017.10.007 · 2018 · External reference
Detecting and avoiding likely false-positive findings—a practical guide
10.1111/brv.12315 · 2017 · External reference
Mixed Models Offer No Freedom from Degrees of Freedom
10.1016/j.tree.2019.12.004 · 2020 · External reference
Generalized linear mixed models: a practical guide for ecology and evolution
10.1016/j.tree.2008.10.008 · 2009 · External reference
SIMR: an R package for power analysis of generalized linear mixed models by simulation.
10.1111/2041-210x.12504 · 2016 · External reference
Power analysis for generalized linear mixed models in ecology and evolution
10.1111/2041-210x.12306 · 2015 · External reference
Understanding Mixed-Effects Models Through Data Simulation.
2021 · External reference
10.1017/9781108776462
10.1017/9781108776462 · 2021 · External reference
In Defense of P Values.
2020 · External reference
In defense of P values
10.1890/13-0590.1 · 2014 · External reference
Curtailing the Use of Preregistration: A Misused Term.
10.1177/1745691619858427 · 2019 · External reference
Extent of Non-Publication in Cohorts of Studies Approved by Research Ethics Committees or Included in Trial Registries.
10.1371/journal.pone.0114023 · 2014 · External reference
Open science challenges, benefits and tips in early career and beyond
10.1371/journal.pbio.3000246 · 2019 · External reference
The FAIR Guiding Principles for scientific data management and stewardship (vol 15, 160018, 2016).
10.1038/s41597-019-0009-6 · 2019 · External reference
Compliance with legal requirement to report clinical trial results on ClinicalTrials.gov: a cohort study
10.1016/s0140-6736(19)33220-9 · 2020 · External reference
Compliance with requirement to report results on the EU Clinical Trials Register: cohort study and web resource.
2018 · External reference
A call to implement preclinical study registration in animal ethics review
10.1371/journal.pbio.3002293 · 2023 · External reference
Pluralism in Ecological Research.
10.1093/biosci/biac089 · 2022 · External reference
Promoting inclusive metrics of success and impact to dismantle a discriminatory reward system in science
10.1371/journal.pbio.3001282 · 2021 · External reference
Reproducibility: expect less of the scientific paper Comment
10.1038/d41586-021-02486-7 · 2021 · External reference
Meta-research: The art of getting it wrong.
10.1002/jrsm.19 · 2010 · External reference
Decoloniality and anti-oppressive practices for a more ethical ecology
10.1038/s41559-021-01460-w · 2021 · External reference
Next steps after airing disagreement on a scientific issue with policy implications: a meta-analysis, multi-lab replication and adversarial collaboration.
10.1186/s12915-023-01567-5 · 2023 · External reference
A guide to prospective meta-analysis.
2019 · External reference
Build up big-team science
10.1038/d41586-022-00150-2 · 2022 · External reference
Quantifying Sources of Variability in Infancy Research Using the Infant-Directed-Speech Preference.
2020 · External reference
Connecting the data landscape of long-term ecological studies: The SPI-Birds data hub
10.1111/1365-2656.13388 · 2021 · External reference
A decade of insights into grassland ecosystem responses to global environmental change.
10.1038/s41559-017-0118 · 2017 · External reference
Citizen science plant observations encode global trait patterns
10.1038/s41559-022-01904-x · 2022 · External reference
Many Labs 3: Evaluating participant pool quality across the academic semester via replication.
10.1016/j.jesp.2015.10.012 · 2016 · External reference
Investigating Variation in Replicability A "Many Labs” Replication Project.
10.1027/1864-9335/a000178 · 2014 · External reference
Meta-regression approximations to reduce publication selection bias.
10.1002/jrsm.1095 · 2014 · External reference
Living systematic review: 1. Introduction-the why, what, when, and how
10.1016/j.jclinepi.2017.08.010 · 2017 · External reference
A new ecosystem for evidence synthesis.
10.1038/s41559-020-1153-2 · 2020 · External reference
Method Reporting with Initials for Transparency (MeRIT) promotes more granularity and accountability for author contributions.
10.1038/s41467-023-37039-1 · 2023 · External reference
’Big team’ science challenges us to reconsider authorship.
10.1038/s41562-023-01572-2 · 2023 · External reference
The reproducibility debate is an opportunity, not a crisis.
10.1186/s13104-022-05942-3 · 2022 · External reference
