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When different researchers study the same research question using the same dataset they may obtain different and potentially even conflicting results.
Resampling-based multiple testing: Examples and methods for p-value adjustment , volume 279
P. H. Westfall and S. S. Young · 1993
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On adjusting p-values for multiplicity
P. H. Westfall, S. S. Young, and S. P. Wright · 1993
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Interval estimation for the difference between independent proportions: comparison of eleven methods
R. G. Newcombe · 1998
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Multiple hypothesis testing in microarray experiments
S. Dudoit, J. P. Shaffer, and J. C. Boldrick · 2003
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Reducing the probability of false positive research findings by pre-publication validation–experience with a large multiple sclerosis database
M. Daumer, U. Held, K. Ickstadt, M. Heinz, S. Schach, and G. Ebers · 2008
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You are what your mother eats: evidence for maternal preconception diet influencing foetal sex in humans
F. Mathews, P. J. Johnson, and A. Neil · 2008
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Cereal-induced gender selection? most likely a multiple testing false positive
S. S. Young, H. Bang, and K. Oktay · 2008
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Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction
A.-L. Boulesteix and C. Strobl · 2009
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False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant
J. P. Simmons, L. D. Nelson, and U. Simonsohn · 2011
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mice: Multivariate imputation by chained equations in r
S. van Buuren and K. Groothuis-Oudshoorn · 2011
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Spirit 2013 statement: defining standard protocol items for clinical trials
A.-W. Chan, J. M. Tetzlaff, D. G. Altman, A. Laupacis, P. C. Gøtzsche, K. Krleža-Jerić, A. Hróbjartsson, H. Mann, K. Dickersin, J. A. Berlin, et al · 2013
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The statistical crisis in science: data-dependent analysis–a" garden of forking paths"–explains why many statistically significant comparisons don’t hold up
A. Gelman and E. Loken · 2014
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Assessment of vibration of effects due to model specification can demonstrate the instability of observational associations
C. J. Patel, B. Burford, and J. P. Ioannidis · 2015
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Increasing transparency through a multiverse analysis
S. Steegen, F. Tuerlinckx, A. Gelman, and W. Vanpaemel · 2016
Cited alongside, same era.
The ASA statement on p-values: context, process, and purpose
R. L. Wasserstein and N. A. Lazar · 2016
Cited alongside, same era.
A manifesto for reproducible science
M. R. Munafò, B. A. Nosek, D. V. Bishop, K. S. Button, C. D. Chambers, N. Percie du Sert, U. Simonsohn, E.-J. Wagenmakers, J. J. Ware, and J. Ioannidis · 2017
Cited alongside, same era.
Probing birth-order effects on narrow traits using specification-curve analysis
J. M. Rohrer, B. Egloff, and S. C. Schmukle · 2017
Cited alongside, same era.
Does retrieval bag use during laparoscopic appendectomy reduce postoperative infection?
A. C. Fields, P. Lu, D. L. Palenzuela, R. Bleday, J. E. Goldberg, J. Irani, J. S. Davids, and N. Melnitchouk · 2018
Cited alongside, same era.
Pre-specification of statistical analysis approaches in published clinical trial protocols was inadequate
Association between marijuana use and cardiovascular disease in us adults
D. Jivanji, M. Mangosing, S. P. Mahoney, G. Castro, J. Zevallos, and J. Lozano · 2020
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Comparing the vibration of effects due to model, data pre-processing and sampling uncertainty on a large data set in personality psychology
S. Klau, C. J. Patel, J. P. Ioannidis, A.-L. Boulesteix, S. Hoffmann, et al · 2020
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Simultaneous inference for multiple marginal generalized estimating equation models
R. Ristl, L. Hothorn, C. Ritz, and M. Posch · 2020
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Association of marijuana use and cardiovascular disease: A behavioral risk factor surveillance system data analysis of 133,706 us adults
S. Shah, S. Patel, S. Paulraj, and D. Chaudhuri · 2020
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Specification curve analysis
U. Simonsohn, J. P. Simmons, and L. D. Nelson · 2020
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L. Greenberg, V. Jairath, R. Pearse, and B. C. Kahan · 2018
Cited alongside, same era.
The preregistration revolution
B. A. Nosek, C. R. Ebersole, A. C. DeHaven, and D. T. Mellor · 2018
Cited alongside, same era.
Many analysts, one data set: Making transparent how variations in analytic choices affect results
R. Silberzahn, E. L. Uhlmann, D. P. Martin, P. Anselmi, F. Aust, E. Awtrey, Š. Bahník, F. Bai, C. Bannard, E. Bonnier, et al · 2018
Cited alongside, same era.
Re: Does retrieval bag use during laparoscopic appendectomy reduce postoperative infection?
C. P. Childers and M. Maggard-Gibbons · 2019
Cited alongside, same era.
mlr3: A modern object-oriented machine learning framework in R
M. Lang, M. Binder, J. Richter, P. Schratz, F. Pfisterer, S. Coors, Q. Au, G. Casalicchio, L. Kotthoff, and B. Bischl · 2019
Cited alongside, same era.
Utilization of a specimen retrieval bag during laparoscopic appendectomy for both uncomplicated and complicated appendicitis is not associated with a decrease in postoperative surgical site infection rates
S. A. Turner, H. S. Jung, and J. E. Scarborough · 2019
Cited alongside, same era.
The conditional permutation test for independence while controlling for confounders
T. B. Berrett, Y. Wang, R. F. Barber, and R. J. Samworth · 2020
Cited alongside, same era.
Perioperative hyperoxyphobia: justified or not? benefits and harms of hyperoxia during surgery
R. P. Weenink, S. W. de Jonge, R. A. van Hulst, T. T. Wingelaar, P.-J. A. van Ooij, R. V. Immink, B. Preckel, and M. W. Hollmann · 2020
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The multiplicity of analysis strategies jeopardizes replicability: lessons learned across disciplines
S. Hoffmann, F. Schönbrodt, R. Elsas, R. Wilson, U. Strasser, and A.-L. Boulesteix · 2021
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Comparing supervised machine learning algorithms for the prediction of partial arterial pressure of oxygen during craniotomy
A. S. Becker-Pennrich, M. M. Mandl, C. Rieder, D. J. Hoechter, K. Dietz, B. P. Geisler, A.-L. Boulesteix, R. Tomasi, and L. C. Hinske · 2022
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Oxygen administration during surgery and postoperative organ injury: observational cohort study
D. R. McIlroy, M. S. Shotwell, M. G. Lopez, M. T. Vaughn, J. S. Olsen, C. Hennessy, J. P. Wanderer, M. S. Semler, T. W. Rice, S. Kheterpal, et al · 2022
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Reducing bias, increasing transparency and calibrating confidence with preregistration
T. E. Hardwicke and E.-J. Wagenmakers · 2023
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Improving the transparency and reliability of observational studies through registration
F. Naudet, C. J. Patel, N. J. DeVito, G. L. Goff, I. A. Cristea, A. Braillon, and S. Hoffmann · 2023
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Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering
T. Ullmann, S. Peschel, P. Finger, C. L. Müller, and A.-L. Boulesteix · 2023
Later among the works it cites.