Scientific method: Statistical errors

Why Most Published Research Findings Are False

Ioannidis, J. P. A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020124 · Cited by 10,505 · see citing works

False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011) · Psychological Science · doi:10.1177/0956797611417632 · Cited by 6,567 · see citing works

P-curve: A key to the file-drawer.

Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014) · Journal of Experimental Psychology: General · doi:10.1037/a0033242 · Cited by 1,500 · see citing works

Marital satisfaction and break-ups differ across on-line and off-line meeting venues

Cacioppo, J. T., Cacioppo, S., Gonzaga, G. C., Ogburn, E. L., & VanderWeele, T. J. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222447110 · Cited by 223 · see citing works

The ASA Statement on <i>p</i> -Values: Context, Process, and Purpose

Wasserstein, R. L., & Lazar, N. A. (2016) · The American Statistician · doi:10.1080/00031305.2016.1154108 · Cited by 6,306 · see citing works

The Extent and Consequences of P-Hacking in Science

Head, M. L., Holman, L., Lanfear, R., Kahn, A. T., & Jennions, M. D. (2015) · PLOS Biology · doi:10.1371/journal.pbio.1002106 · Cited by 1,388 · see citing works

GRADE guidelines 26: informative statements to communicate the findings of systematic reviews of interventions

Santesso, N., Glenton, C., Dahm, P., Garner, P., Akl, E. A., Alper, B., Brignardello-Petersen, R., Carrasco-Labra, A., De Beer, H., Hultcrantz, M., et al. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.10.014 · Cited by 1,198 · see citing works

What are the odds? A practical guide to computing and reporting Bayes factors

Jarosz, A. F., & Wiley, J. (2014) · The Journal of Problem Solving · doi:10.7771/1932-6246.1167 · Cited by 1,101 · see citing works

Organoids

Zhao, Z., Chen, X., Dowbaj, A. M., Sljukic, A., Bratlie, K., Lin, L., Fong, E. L. S., Balachander, G. M., Chen, Z., Soragni, A., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00174-y · Cited by 1,001 · see citing works

Indices of Effect Existence and Significance in the Bayesian Framework

Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., & Lüdecke, D. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02767 · Cited by 983 · see citing works

<scp>eD</scp> octor: machine learning and the future of medicine

Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., & Asadi, H. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12822 · Cited by 956 · see citing works

How to Make More Published Research True

Ioannidis, J. P. A. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001747 · Cited by 760 · see citing works

Best practices in data analysis and sharing in neuroimaging using MRI

Nichols, T. E., Das, S., Eickhoff, S. B., Evans, A. C., Glatard, T., Hanke, M., Kriegeskorte, N., Milham, M. P., Poldrack, R. A., Poline, J.-B., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4500 · Cited by 714 · see citing works

Local Standards for Sample Size at CHI

Caine, K. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858498 · Cited by 657 · see citing works

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., & Liotta, A. (2018) · Nature Communications · doi:10.1038/s41467-018-04316-3 · Cited by 524 · see citing works

Guidelines for experimental models of myocardial ischemia and infarction

Lindsey, M. L., Bolli, R., Canty, J. M., Du, X.-J., Frangogiannis, N. G., Frantz, S., Gourdie, R. G., Holmes, J. W., Jones, S. P., Kloner, R. A., et al. (2018) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00335.2017 · Cited by 526 · see citing works

Detecting and avoiding likely false‐positive findings – a practical guide

Forstmeier, W., Wagenmakers, E., & Parker, T. H. (2017) · Biological Reviews · doi:10.1111/brv.12315 · Cited by 436 · see citing works

Treatment selection in depression.

Cohen, Z. D., & DeRubeis, R. J. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050817-084746 · Cited by 435 · see citing works

Machine learning in concrete science: applications, challenges, and best practices

Li, Z., Yoon, J., Zhang, R., Rajabipour, F., Srubar III, W. V., Dabo, I., & Radlińska, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00810-x · Cited by 388 · see citing works

Storyline approach to the construction of regional climate change information

Shepherd, T. G. (2019) · Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.2019.0013 · Cited by 331 · see citing works

Using prediction markets to estimate the reproducibility of scientific research

Dreber, A., Pfeiffer, T., Almenberg, J., Isaksson, S., Wilson, B., Chen, Y., Nosek, B. A., & Johannesson, M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1516179112 · Cited by 331 · see citing works

Beyond small, medium, or large: points of consideration when interpreting effect sizes

Bakker, A., Cai, J., English, L., Kaiser, G., Mesa, V., & Van Dooren, W. (2019) · Educational Studies in Mathematics · doi:10.1007/s10649-019-09908-4 · Cited by 302 · see citing works

Designing awe in virtual reality: an experimental study

Chirico, A., Ferrise, F., Cordella, L., & Gaggioli, A. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.02351 · Cited by 276 · see citing works

Environmental Chemical Exposures and Autism Spectrum Disorders: A Review of the Epidemiological Evidence

Kalkbrenner, A. E., Schmidt, R. J., & Penlesky, A. C. (2014) · Current Problems in Pediatric and Adolescent Health Care · doi:10.1016/j.cppeds.2014.06.001 · Cited by 268 · see citing works

The search for human pheromones: The lost decades and the necessity of returning to first principles

Wyatt, T. D. (2015) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2014.2994 · Cited by 123 · see citing works

Prefrontal cortex markers of suicidal vulnerability in mood disorders: a model-based structural neuroimaging study with a translational perspective

Ding, Y., Lawrence, N., Olié, E., Cyprien, F., le Bars, E., Bonafé, A., Phillips, M. L., Courtet, P., & Jollant, F. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.1 · Cited by 124 · see citing works

CyberBERT: BERT for cyberbullying identification

Paul, S., & Saha, S. (2022) · Multimedia Systems · doi:10.1007/s00530-020-00710-4 · Cited by 116 · see citing works

A Gentle Introduction to the Comparison Between Null Hypothesis Testing and Bayesian Analysis: Reanalysis of Two Randomized Controlled Trials

Bendtsen, M. (2018) · Journal of Medical Internet Research · doi:10.2196/10873 · Cited by 105 · see citing works

Improving transparency and scientific rigor in academic publishing

Prager, E. M., Chambers, K. E., Plotkin, J. L., McArthur, D. L., Bandrowski, A. E., Bansal, N., Martone, M. E., Bergstrom, H. C., Bespalov, A., & Graf, C. (2019) · Journal of Neuroscience Research · doi:10.1002/jnr.24340 · Cited by 83 · see citing works

The deterministic part of the seventh International Planning Competition

Linares López, C., Jiménez Celorrio, S., & García Olaya, Á. (2015) · Artificial Intelligence · doi:10.1016/j.artint.2015.01.004 · Cited by 71 · see citing works

Exploratory studies of the potential anti-cancer effects of creatine

Campos-Ferraz, P. L., Gualano, B., das Neves, W., Andrade, I. T., Hangai, I., Pereira, R. T. S., Bezerra, R. N., Deminice, R., Seelaender, M., & Lancha, A. H. (2016) · Amino Acids · doi:10.1007/s00726-016-2180-9 · Cited by 47 · see citing works

C-peptide and beta-cell autoantibody testing prior to initiating continuous subcutaneous insulin infusion pump therapy did not improve utilization or medical costs among older adults with diabetes mellitus

Gill, M., Chhabra, H., Shah, M., Zhu, C., & Grunberger, G. (2018) · Endocrine Practice · doi:10.4158/ep-2017-0220 · Cited by 8 · see citing works

Multisource and temporal variability in Portuguese hospital administrative datasets: Data quality implications

Souza, J., Caballero, I., Vasco Santos, J., Lobo, M., Pinto, A., Viana, J., Sáez, C., Lopes, F., & Freitas, A. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104242 · Cited by 7 · see citing works

<i>P</i> hacking in biology: An open secret

Veresoglou, S. D. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1512689112 · Cited by 7 · see citing works

Untitled work

Grützmann (2022) · doi:10.1016/j.bpsgos.2021.07.003 · Cited by 3 · see citing works