References
Aczel, B., Palfi, B., Szollosi, A., Kovacs, M., Szaszi, B., Szecsi, P.,
Zrubka, M., Gronau, Q. F., Bergh, D. van den, & Wagenmakers, E.-J.
(2018). Quantifying support for the null hypothesis in psychology: An
empirical investigation. Advances in Methods and Practices in
Psychological Science, 1(3), 357–366. https://doi.org/10.1177/2515245918773742
Allard, A. (2018). Analytic-GRIMMER: A new way of
testing the possibility of standard deviations. https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/
Altman, D. G., & Bland, J. M. (1995). Absence of evidence is not
evidence of absence. BMJ, 311(7003), 485. https://doi.org/10.1136/bmj.311.7003.485
Altman, D. G., & Bland, J. M. (2005). Standard deviations and
standard errors. BMJ, 331(7521), 903. https://doi.org/10.1136/bmj.331.7521.903
Anaya, J. (2016). The GRIMMER test: A method for
testing the validity of reported measures of variability. PeerJ
Preprints. https://doi.org/10.7287/peerj.preprints.2400v1
Armitage, P., McPherson, C. K., & Rowe, B. C. (1969). Repeated
significance tests on accumulating data. Journal of the Royal
Statistical Society: Series A (General), 132(2), 235–244.
https://doi.org/10.2307/2343787
Badenes-Ribera, L., Frías-Navarro, D., Iotti, B., Bonilla-Campos, A.,
& Longobardi, C. (2016). Misconceptions of the
p-value among Chilean and
Italian academic psychologists. Frontiers in
Psychology, 7, Article 1247. https://doi.org/10.3389/fpsyg.2016.01247
Badenes-Ribera, L., Frías-Navarro, D., Monterde-i-Bort, H., &
Pascual-Soler, M. (2015). Interpretation of the p
value: A national survey study in academic psychologists from
Spain. Psicothema, 27(3), 290–295. https://doi.org/10.7334/psicothema2014.283
Bainbridge, T. F., Ludeke, S. G., & Smillie, L. D. (2022).
Evaluating the Big Five as an organizing framework for
commonly used psychological trait scales. Journal of Personality and
Social Psychology, 122(4), 749–777. https://doi.org/10.1037/pspp0000395
Barde, M. P., & Barde, P. J. (2012). What to use to express the
variability of data: Standard deviation or standard error of mean?
Perspectives in Clinical Research, 3(3), 113–116. https://doi.org/10.4103/2229-3485.100662
Baron-Cohen, S., Bowen, D. C., Holt, R. J., Allison, C., Auyeung, B.,
Lombardo, M. V., Smith, P., & Lai, M.-C. (2015). The “reading
the mind in the eyes” test: Complete absence of typical sex
difference in 400 men and women with autism. PLoS ONE,
10(8), e0136521. https://doi.org/10.1371/journal.pone.0136521
Bartoš, F., & Schimmack, U. (2022). Z-curve 2.0: Estimating
replication rates and discovery rates. Meta-Psychology,
6. https://doi.org/10.15626/MP.2021.2720
Belia, S., Fidler, F., Williams, J., & Cumming, G. (2005).
Researchers misunderstand confidence intervals and standard error bars.
Psychological Methods, 10(4), 389–396. https://doi.org/10.1037/1082-989X.10.4.389
Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A.,
Wagenmakers, E.-J., Berk, R., Bollen, K. A., Brembs, B., Brown, L.,
Camerer, C., Cesarini, D., Chambers, C. D., Clyde, M., Cook, T. D., De
Boeck, P., Dienes, Z., Dreber, A., Easwaran, K., Efferson, C., …
Johnson, V. E. (2018). Redefine statistical significance. Nature
Human Behaviour, 2(1), 6–10. https://doi.org/10.1038/s41562-017-0189-z
Bhatia, R., & Davis, C. (2000). A better bound on the variance.
The American Mathematical Monthly, 107(4), 353–357. https://doi.org/10.1080/00029890.2000.12005203
Bishop, D. V. M., & Thompson, P. A. (2016). Problems in using
p-curve analysis and text-mining to detect rate of
p-hacking and evidential value. PeerJ,
4, Article e1715. https://doi.org/10.7717/peerj.1715
Bogdan, P. C. (2025). One decade into the replication crisis, how have
psychological results changed? Advances in Methods and Practices in
Psychological Science, 8(2), Article 25152459251323480. https://doi.org/10.1177/25152459251323480
Brown, N. J. L. (2026). Brown’s
Law. Personal communication to Ian Hussey.
Brown, N. J. L., & Heathers, J. A. J. (2017). The GRIM
test: A simple technique detects numerous anomalies in the reporting of
results in psychology. Social Psychological and Personality
Science, 8(4), 363–369. https://doi.org/10.1177/1948550616673876
Brunner, J., & Schimmack, U. (2020). Estimating population mean
power under conditions of heterogeneity and selection for significance.
Meta-Psychology, 4. https://doi.org/10.15626/MP.2018.874
Bruns, S. B., & Ioannidis, J. P. A. (2016).
p-curve and p-hacking in
observational research. PLOS ONE, 11(2), Article
e0149144. https://doi.org/10.1371/journal.pone.0149144
Brysbaert, M. (2024). Questioning the exclusive focus on the
Hu and Bentler norms in factor analysis:
Practice-oriented Likert scale indicators based on an
analysis of 161 datasets. PsyArXiv. https://doi.org/10.31234/osf.io/m72w8
Bürkner, P.-C., & Vuorre, M. (2019). Ordinal regression models in
psychology: A tutorial. Advances in Methods and Practices in
Psychological Science, 2(1), 77–101. https://doi.org/10.1177/2515245918823199
Cassidy, S. A., Dimova, R., Giguère, B., Spence, J. R., & Stanley,
D. J. (2019). Failing grade: 89% of introduction-to-psychology textbooks
that define or explain statistical significance do so incorrectly.
Advances in Methods and Practices in Psychological Science,
2(3), 233–239. https://doi.org/10.1177/2515245919858072
Cochrane. (n.d.-a). Cochrane database of systematic reviews:
Editorial policies. Cochrane Library. Retrieved https://www.cochranelibrary.com/cdsr/editorial-policies
Cochrane. (n.d.-b). Policy for managing potentially problematic
studies: Implementation guidance. Cochrane Library editorial
policy. Retrieved https://www.cochranelibrary.com/cdsr/editorial-policies/problematic-studies-implementation-guidance
Cochrane. (2025). Methods in Cochrane. https://www.cochrane.org/authors/methods-cochrane
Cohen, J. (1994). The earth is round (p < .05).
American Psychologist, 49(12), 997–1003. https://doi.org/10.1037/0003-066X.49.12.997
Cox, D. R. (2006). Principles of statistical inference.
Cambridge University Press. https://doi.org/10.1017/CBO9780511813559
Cumming, G. (2008). Replication and p intervals:
p values predict the future only vaguely, but
confidence intervals do much better. Perspectives on Psychological
Science, 3(4), 286–300. https://doi.org/10.1111/j.1745-6924.2008.00079.x
Elk, M. van, & Fried, E. I. (2023). History repeating: Guidelines to
address common problems in psychedelic science. Therapeutic Advances
in Psychopharmacology, 13. https://doi.org/10.1177/20451253231198466
Erdfelder, E., & Heck, D. W. (2019). Detecting evidential value and
p-hacking with the p-curve
tool: A word of caution. Zeitschrift für
Psychologie, 227(4), 249–260. https://doi.org/10.1027/2151-2604/a000383
Falk, R., & Greenbaum, C. W. (1995). Significance tests die hard:
The amazing persistence of a probabilistic misconception. Theory
& Psychology, 5(1), 75–98. https://doi.org/10.1177/0959354395051004
Ferrari, S., & Cribari-Neto, F. (2004). Beta regression for
modelling rates and proportions. Journal of Applied Statistics,
31(7), 799–815. https://doi.org/10.1080/0266476042000214501
Francis, G. (2014). The frequency of excess success for articles in
Psychological Science.
Psychonomic Bulletin & Review, 21(5), 1180–1187.
https://doi.org/10.3758/s13423-014-0601-x
Francis, G. (2016). Confirming the appearance of excess success: Reply
to van Boxtel and Koch (2016). Psychonomic
Bulletin & Review, 23(6), 2010–2013. https://doi.org/10.3758/s13423-016-1137-z
Francis, G., Tanzman, J., & Matthews, W. J. (2014). Excess success
for psychology articles in the journal Science.
PLOS ONE, 9(12), Article e114255. https://doi.org/10.1371/journal.pone.0114255
Franco, A., Malhotra, N., & Simonovits, G. (2014). Publication bias
in the social sciences: Unlocking the file drawer. Science,
345(6203), 1502–1505. https://doi.org/10.1126/science.1255484
Gallardo-Gómez, D., Richardson, R., & Dwan, K. (2024). Standardized
mean differences in meta-analysis: A tutorial. Cochrane Evidence
Synthesis and Methods, 2(3), e12047. https://doi.org/10.1002/cesm.12047
García-Pérez, M. A., & Alcalá-Quintana, R. (2016). The
interpretation of scholars’ interpretations of confidence intervals:
Criticism, replication, and extension of Hoekstra et al.
(2014). Frontiers in Psychology, 7, Article 1042. https://doi.org/10.3389/fpsyg.2016.01042
Gelman, A., & Stern, H. (2006). The difference between
“significant” and “not significant” is not
itself statistically significant. The American Statistician,
60(4), 328–331. https://doi.org/10.1198/000313006X152649
Gigerenzer, G. (2004). Mindless statistics. The Journal of
Socio-Economics, 33(5), 587–606. https://doi.org/10.1016/j.socec.2004.09.033
Gigerenzer, G. (2018). Statistical rituals: The replication delusion and
how we got there. Advances in Methods and Practices in Psychological
Science, 1(2), 198–218. https://doi.org/10.1177/2515245918771329
Goodman, S. N. (1992). A comment on replication,
p-values and evidence. Statistics in
Medicine, 11(7), 875–879. https://doi.org/10.1002/sim.4780110705
Gordon, M., Viganola, D., Dreber, A., Johannesson, M., & Pfeiffer,
T. (2021). Predicting replicability—Analysis
of survey and prediction market data from large-scale forecasting
projects. PLOS ONE, 16(4), Article e0248780. https://doi.org/10.1371/journal.pone.0248780
Greenberg, S. A. (2009). How citation distortions create unfounded
authority: Analysis of a citation network. BMJ, 339,
b2680. https://doi.org/10.1136/bmj.b2680
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C.,
Goodman, S. N., & Altman, D. G. (2016). Statistical tests,
P values, confidence intervals, and power: A guide
to misinterpretations. European Journal of Epidemiology,
31(4), 337–350. https://doi.org/10.1007/s10654-016-0149-3
Haller, H., & Krauss, S. (2002). Misinterpretations of significance:
A problem students share with their teachers? Methods of
Psychological Research Online, 7(1), 1–20. https://epub.uni-regensburg.de/34338/
Hartgerink, C. H. J., van Aert, R. C. M., Nuijten, M. B., Wicherts, J.
M., & van Assen, M. A. L. M. (2016). Distributions of
p-values smaller than .05 in psychology: What is
going on? PeerJ, 4, Article e1935. https://doi.org/10.7717/peerj.1935
Hauer, E. (2004). The harm done by tests of significance. Accident
Analysis & Prevention, 36(3), 495–500. https://doi.org/10.1016/S0001-4575(03)00036-8
Heathers, J. A. J. (2024). Approximately 1 in 7 scientific papers
are fake. https://osf.io/5rf2m/
Heathers, J. A. J., & Grimes, D. R. (2025). GRIM-U:
A GRIM-like observation to establish impossible p values
from ranked tests (1). Medical Evidence Project. https://medicalevidenceproject.org/grim-u-observation-establish-impossible-p-values-ranked-tests/
Hussey, I. (2026a). Analyticgrimu: Granularity testing for
Mann-Whitney U-test reported results. https://doi.org/10.5281/zenodo.21434617
Hussey, I. (2026b). Strait: Bounds checks for reported summary
statistics. https://doi.org/10.5281/zenodo.21439905
Hussey, I., Alsalti, T., Bosco, F., Elson, M., & Arslan, R. C.
(2025). An aberrant abundance of Cronbach’s
alpha values at .70. Advances in Methods and Practices in
Psychological Science, 8(1), Article 25152459241287123. https://doi.org/10.1177/25152459241287123
Ioannidis, J. P. A., & Trikalinos, T. A. (2007). An exploratory test
for an excess of significant findings. Clinical Trials,
4(3), 245–253. https://doi.org/10.1177/1740774507079441
John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the
prevalence of questionable research practices with incentives for truth
telling. Psychological Science, 23(5), 524–532. https://doi.org/10.1177/0956797611430953
Johnson, M. H., Dziurawiec, S., Ellis, H., & Morton, J. (1991).
Newborns’ preferential tracking of face-like stimuli and its subsequent
decline. Cognition, 40(1), 1–19. https://doi.org/10.1016/0010-0277(91)90045-6
Jung, L. (2026). Scrutiny: Error detection in science. https://github.com/lhdjung/scrutiny
Kadlec, D., Sainani, K. L., & Nimphius, S. (2023). With great power
comes great responsibility: Common errors in meta-analyses and
meta-regressions in strength & conditioning research. Sports
Medicine, 53(2), 313–325. https://doi.org/10.1007/s40279-022-01766-0
Kanellopoulou, A., Evrenoglou, T., Metelli, S., & Chaimani, A.
(2025). Common statistical errors in systematic reviews: A tutorial.
Cochrane Evidence Synthesis and Methods, 3(5), e70013.
https://doi.org/10.1002/cesm.70013
Ko, W.-R., Hung, W.-T., Chang, H.-C., & Lin, L.-Y. (2014).
Inappropriate use of standard error of the mean when reporting
variability of study samples: A critical evaluation of four selected
journals of obstetrics and gynecology. Taiwanese Journal of
Obstetrics and Gynecology, 53(1), 26–29. https://doi.org/10.1016/j.tjog.2013.04.035
Kruschke, J. K. (2015). Doing Bayesian data analysis: A
tutorial with R, JAGS, and
Stan (2nd ed.). Academic Press. https://doi.org/10.1016/C2012-0-00477-2
Kubinec, R. (2023). Ordered beta regression: A parsimonious,
well-fitting model for continuous data with lower and upper bounds.
Political Analysis, 31(4), 519–536. https://doi.org/10.1017/pan.2022.20
Lakens, D. (2014). Performing high-powered studies efficiently with
sequential analyses. European Journal of Social Psychology,
44(7), 701–710. https://doi.org/10.1002/ejsp.2023
Lakens, D. (2015). On the challenges of drawing conclusions from
p-values just below 0.05. PeerJ,
3, Article e1142. https://doi.org/10.7717/peerj.1142
Lakens, D. (2017). Equivalence tests: A practical primer for t tests,
correlations, and meta-analyses. Social Psychological and
Personality Science, 8(4), 355–362. https://doi.org/10.1177/1948550617697177
Lakens, D. (2022). Improving your statistical inferences
(Version 1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6409077
Lakens, D., Adolfi, F. G., Albers, C. J., Anvari, F., Apps, M. A. J.,
Argamon, S. E., Baguley, T., Becker, R. B., Benning, S. D., Bradford, D.
E., Buchanan, E. M., Caldwell, A. R., Van Calster, B., Carlsson, R.,
Chen, S.-C., Chung, B., Colling, L. J., Collins, G. S., Crook, Z., …
Zwaan, R. A. (2018). Justify your alpha. Nature Human
Behaviour, 2(3), 168–171. https://doi.org/10.1038/s41562-018-0311-x
Lakens, D., & Etz, A. J. (2017). Too true to be bad: When sets of
studies with significant and nonsignificant findings are probably true.
Social Psychological and Personality Science, 8(8),
875–881. https://doi.org/10.1177/1948550617693058
Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence
testing for psychological research: A tutorial. Advances in Methods
and Practices in Psychological Science, 1(2), 259–269. https://doi.org/10.1177/2515245918770963
Leggett, N. C., Thomas, N. A., Loetscher, T., & Nicholls, M. E. R.
(2013). The life of p: "Just significant" results
are on the rise. Quarterly Journal of Experimental Psychology,
66(12), 2303–2309. https://doi.org/10.1080/17470218.2013.863371
Liddell, T. M., & Kruschke, J. K. (2018). Analyzing ordinal data
with metric models: What could possibly go wrong? Journal of
Experimental Social Psychology, 79, 328–348. https://doi.org/10.1016/j.jesp.2018.08.009
Lyu, X.-K., Xu, Y., Zhao, X.-F., Zuo, X.-N., & Hu, C.-P. (2020).
Beyond psychology: Prevalence of p value and
confidence interval misinterpretation across different fields.
Journal of Pacific Rim Psychology, 14, Article e6. https://doi.org/10.1017/prp.2019.28
Lyu, Z., Peng, K., & Hu, C.-P. (2018).
P-value, confidence intervals, and statistical
inference: A new dataset of misinterpretation. Frontiers in
Psychology, 9, Article 868. https://doi.org/10.3389/fpsyg.2018.00868
Masicampo, E. J., & Lalande, D. R. (2012). A peculiar prevalence of
p values just below .05. Quarterly Journal of
Experimental Psychology, 65(11), 2271–2279. https://doi.org/10.1080/17470218.2012.711335
McElreath, R. (2020). Statistical rethinking: A
Bayesian course with examples in R and
Stan (2nd ed.). CRC Press. https://doi.org/10.1201/9780429029608
McShane, B. B., Böckenholt, U., & Hansen, K. T. (2016). Adjusting
for publication bias in meta-analysis: An evaluation of selection
methods and some cautionary notes. Perspectives on Psychological
Science, 11(5), 730–749. https://doi.org/10.1177/1745691616662243
McShane, B. B., & Gal, D. (2017). Statistical significance and the
dichotomization of evidence. Journal of the American Statistical
Association, 112(519), 885–895. https://doi.org/10.1080/01621459.2017.1289846
Miller, J., & Ulrich, R. (2016). Interpreting confidence intervals:
A comment on Hoekstra, Morey,
Rouder, and Wagenmakers (2014).
Psychonomic Bulletin & Review, 23(1), 124–130. https://doi.org/10.3758/s13423-015-0859-7
Moran, T., Hughes, S., Hussey, I., et al. (2021). Incidental attitude
formation via the surveillance task: A preregistered replication of the
Olson and Fazio (2001) study.
Psychological Science, 32(1), 120–131. https://doi.org/10.1177/0956797620968526
Morey, R. D., & Davis-Stober, C. P. (2026). On the poor statistical
properties of the P-curve meta-analytic procedure.
Journal of the American Statistical Association,
121(553), 741–753. https://doi.org/10.1080/01621459.2025.2544397
Morey, R. D., & Lakens, D. (2016). Why most of psychology is
statistically unfalsifiable. Zenodo. https://doi.org/10.5281/zenodo.838685
Morton, J., & Johnson, M. H. (1991). CONSPEC and
CONLERN: A two-process theory of infant face recognition.
Psychological Review, 98(2), 164–181. https://doi.org/10.1037/0033-295X.98.2.164
Murphy, S. L., Merz, R., Reimann, L.-E., & Fernández, A. (2025).
Nonsignificance misinterpreted as an effect’s absence in psychology:
Prevalence and temporal analyses. Royal Society Open Science,
12(3), 242167. https://doi.org/10.1098/rsos.242167
Nagele, P. (2003). Misuse of standard error of the mean
(SEM) when reporting variability of a sample: A critical
evaluation of four anaesthesia journals. British Journal of
Anaesthesia, 90(4), 514–516. https://doi.org/10.1093/bja/aeg087
Nieuwenhuis, S., Forstmann, B. U., & Wagenmakers, E.-J. (2011).
Erroneous analyses of interactions in neuroscience: A problem of
significance. Nature Neuroscience, 14(9), 1105–1107.
https://doi.org/10.1038/nn.2886
Nuijten, M. B., & Epskamp, S. (2025). Statcheck: Extract
statistics from articles and recompute p-values. https://github.com/MicheleNuijten/statcheck
Nuijten, M. B., Hartgerink, C. H. J., van Assen, M. A. L. M., Epskamp,
S., & Wicherts, J. M. (2016). The prevalence of statistical
reporting errors in psychology (1985–2013). Behavior Research
Methods, 48(4), 1205–1226. https://doi.org/10.3758/s13428-015-0664-2
Oakes, M. (1986). Statistical inference: A commentary for the social
and behavioural sciences. Wiley.
Oldfield, R. C. (1971). The assessment and analysis of handedness: The
Edinburgh inventory. Neuropsychologia,
9(1), 97–113. https://doi.org/10.1016/0028-3932(71)90067-4
Olsson-Collentine, A., van Assen, M. A. L. M., & Hartgerink, C. H.
J. (2019). The prevalence of marginally significant results in
psychology over time. Psychological Science, 30(4),
576–586. https://doi.org/10.1177/0956797619830326
Open Science Collaboration. (2015). Estimating the reproducibility of
psychological science. Science, 349(6251), Article
aac4716. https://doi.org/10.1126/science.aac4716
Ospina, R., & Ferrari, S. L. P. (2012). A general class of
zero-or-one inflated beta regression models. Computational
Statistics & Data Analysis, 56(6), 1609–1623. https://doi.org/10.1016/j.csda.2011.10.005
Otte, W. M., Vinkers, C. H., Habets, P. C., van IJzendoorn, D. G. P.,
& Tijdink, J. K. (2022). Analysis of 567,758 randomized controlled
trials published over 30 years reveals trends in phrases used to discuss
results that do not reach statistical significance. PLOS
Biology, 20(2), Article e3001562. https://doi.org/10.1371/journal.pbio.3001562
Palhano-Fontes, F., Barreto, D., Onias, H., Andrade, K. C., Novaes, M.
M., Pessoa, J. A., Mota-Rolim, S. A., Osório, F. L., Sanches, R.,
Santos, R. G. dos, Tófoli, L. F., Oliveira Silveira, G. de, Yonamine,
M., Riba, J., Santos, F. R., Silva-Junior, A. A., Alchieri, J. C.,
Galvão-Coelho, N. L., Lobão-Soares, B., … Araújo, D. B. (2019). Rapid
antidepressant effects of the psychedelic ayahuasca in
treatment-resistant depression: A randomized placebo-controlled trial.
Psychological Medicine, 49(4), 655–663. https://doi.org/10.1017/S0033291718001356
Pek, J., Wu, H., Liu, Y., Dusenbery, K. J., & Wegener, D. T. (2026).
What does a Z-curve analysis tell us?
Cognition and Emotion. Advance online publication. https://doi.org/10.1080/02699931.2026.2613139
Plöderl, M., Horowitz, M. A., & Hengartner, M. P. (2023). Re:
“A multicenter double-blind, placebo-controlled trial
of escitalopram in children and adolescents with generalized anxiety
disorder” by Strawn et al.—Concerning
harm–benefit ratio in a recent trial about escitalopram for generalized
anxiety disorder. Journal of Child and Adolescent
Psychopharmacology, 33(7), 295–296. https://doi.org/10.1089/cap.2023.0029
Preusser, D. F., Leaf, W. A., DeBartolo, K. B., Blomberg, R. D., &
Levy, M. M. (1982). The effect of right-turn-on-red on pedestrian and
bicyclist accidents. Journal of Safety Research,
13(2), 45–55. https://doi.org/10.1016/0022-4375(82)90001-9
Pritschet, L., Powell, D., & Horne, Z. (2016). Marginally
significant effects as evidence for hypotheses: Changing attitudes over
four decades. Psychological Science, 27(7), 1036–1042.
https://doi.org/10.1177/0956797616645672
Rosenthal, R. (1979). The file drawer problem and tolerance for null
results. Psychological Bulletin, 86(3), 638–641. https://doi.org/10.1037/0033-2909.86.3.638
Rosnow, R. L., & Rosenthal, R. (1989). Statistical procedures and
the justification of knowledge in psychological science. American
Psychologist, 44(10), 1276–1284. https://doi.org/10.1037/0003-066X.44.10.1276
Sandercock, G. R. H. (2024). The standard error/standard deviation
mix-up: Potential impacts on meta-analyses in sports medicine.
Sports Medicine, 54(6), 1723–1732. https://doi.org/10.1007/s40279-023-01989-9
Scheel, A. M., Schijen, M. R. M. J., & Lakens, D. (2021). An excess
of positive results: Comparing the standard psychology literature with
Registered Reports. Advances in Methods
and Practices in Psychological Science, 4(2), Article
25152459211007467. https://doi.org/10.1177/25152459211007467
Schimmack, U. (2012). The ironic effect of significant results on the
credibility of multiple-study articles. Psychological Methods,
17(4), 551–566. https://doi.org/10.1037/a0029487
Schimmack, U., & Soto, M. D. (2026). A response to Pek
et al.’s commentary on Z-curve: Clarifying the
assumptions of selection models. Cognition and Emotion. Advance
online publication. https://doi.org/10.1080/02699931.2026.2678998
Schmidt, T. (2017). Sources of false positives and false negatives
in the STATCHECK algorithm: Reply to Nuijten
et al. (2016). arXiv. https://doi.org/10.48550/arXiv.1610.01010
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011).
False-positive psychology: Undisclosed flexibility in data collection
and analysis allows presenting anything as significant.
Psychological Science, 22(11), 1359–1366. https://doi.org/10.1177/0956797611417632
Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014a).
p-curve and effect size: Correcting for
publication bias using only significant results. Perspectives on
Psychological Science, 9(6), 666–681. https://doi.org/10.1177/1745691614553988
Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014b).
P-curve: A key to the file-drawer. Journal of
Experimental Psychology: General, 143(2), 534–547. https://doi.org/10.1037/a0033242
Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2015). Better
P-curves: Making P-curve
analysis more robust to errors, fraud, and ambitious
P-hacking, a reply to Ulrich and
Miller (2015). Journal of Experimental Psychology:
General, 144(6), 1146–1152. https://doi.org/10.1037/xge0000104
Smithson, M., & Verkuilen, J. (2006). A better lemon squeezer? Maximum-likelihood regression with
beta-distributed dependent variables. Psychological Methods,
11(1), 54–71. https://doi.org/10.1037/1082-989X.11.1.54
Stefan, A. M., & Schönbrodt, F. D. (2023). Big little lies: A
compendium and simulation of p-hacking strategies.
Royal Society Open Science, 10(2), Article 220346. https://doi.org/10.1098/rsos.220346
Sterling, T. D. (1959). Publication decisions and their possible effects
on inferences drawn from tests of significance—or vice versa.
Journal of the American Statistical Association,
54(285), 30–34. https://doi.org/10.1080/01621459.1959.10501497
Strack, F., Martin, L. L., & Stepper, S. (1988). Inhibiting and
facilitating conditions of the human smile: A nonobtrusive test of the
facial feedback hypothesis. Journal of Personality and Social
Psychology, 54(5), 768–777. https://doi.org/10.1037/0022-3514.54.5.768
Strawn, J. R., Moldauer, L., Hahn, R. D., Wise, A., Bertzos, K.,
Eisenberg, B., Greenberg, E., Liu, C., Gopalkrishnan, M., McVoy, M.,
& Knutson, J. A. (2023). A multicenter double-blind,
placebo-controlled trial of escitalopram in children and adolescents
with generalized anxiety disorder. Journal of Child and Adolescent
Psychopharmacology, 33(3), 91–100. https://doi.org/10.1089/cap.2023.0004
Travis, K. (2026). Written testimony of Kate Travis,
managing editor, Retraction Watch, for the hearing titled
“The State of Scientific Publishing:
Assessing Trends, Emerging Issues, and Policy
Considerations”. Subcommittee on Investigations and
Oversight, Committee on Science, Space, and Technology, U.S. House of
Representatives. https://republicans-science.house.gov/_cache/files/8/8/8893b8d8-cce4-46ac-b43a-42f2407587b8/C065DE0F54E66B2C273BB4CE31899F1B3EC2FEC433F320AE7B03D8E6DE1112A8.ms.-kate-travis-testimony.pdf
Ulrich, R., & Miller, J. (2015). p-hacking by
post hoc selection with multiple opportunities: Detectability by
skewness test? Comment on Simonsohn, Nelson,
and Simmons (2014). Journal of Experimental Psychology:
General, 144(6), 1137–1145. https://doi.org/10.1037/xge0000086
van Aert, R. C. M., Wicherts, J. M., & van Assen, M. A. L. M.
(2016). Conducting meta-analyses based on p
values: Reservations and recommendations for applying
p-uniform and p-curve.
Perspectives on Psychological Science, 11(5), 713–729.
https://doi.org/10.1177/1745691616650874
van Boxtel, J. J. A., & Koch, C. (2016). Reevaluating excess success
in psychological science. Psychonomic Bulletin & Review,
23(5), 1602–1606. https://doi.org/10.3758/s13423-016-1010-0
Viechtbauer, W. (2010). Conducting meta-analyses in R with
the metafor package. Journal of Statistical Software,
36(3), 1–48. https://doi.org/10.18637/jss.v036.i03
Wagenmakers, E.-J., Beek, T., Dijkhoff, L., Gronau, Q. F., Acosta, A.,
Adams, R. B., Albohn, D. N., Allard, E. S., Benning, S. D.,
Blouin-Hudon, E.-M., et al. (2016). Registered replication report:
Strack, Martin, & Stepper (1988). Perspectives on
Psychological Science, 11(6), 917–928. https://doi.org/10.1177/1745691616674458
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA
statement on p-values: Context, process, and
purpose. The American Statistician, 70(2), 129–133. https://doi.org/10.1080/00031305.2016.1154108
Wilkinson, J., Bero, L., Heal, C., Flemyng, E., Antoniou, G. A.,
Aburrow, T., Alfirevic, Z., Avenell, A., Barbour, V., Berghella, V.,
Bishop, D. V. M., Bordewijk, E. M., Brown, N. J. L., Christopher, J.,
Clarke, M., Dahly, D., Dennis, J., Dicker, P., Dumville, J., … Kirkham,
J. J. (2026). INSPECT-SR tool for assessing trustworthiness
of randomised controlled trials. BMJ, 394, e100611. https://doi.org/10.1136/bmj-2026-100611
Wilkinson, N. M., Kiessling, J., & Gredebäck, G. (2025). Is
there an innate face detection module in the human newborn?
A systematic review, meta-analysis and theoretical
examination of the newborn face preference and its implications
[Unpublished manuscript].
Wullschleger, M., Aghlmandi, S., Egger, M., & Zwahlen, M. (2014).
High incorrect use of the standard error of the mean (SEM)
in original articles in three cardiovascular journals evaluated for
2012. PLOS ONE, 9(10), e110364. https://doi.org/10.1371/journal.pone.0110364
Zee, T. van der, Anaya, J., & Brown, N. J. L. (2017). Statistical
heartburn: An attempt to digest four pizza publications from the
Cornell Food and Brand
Lab. BMC Nutrition, 3(1), Article 54. https://doi.org/10.1186/s40795-017-0167-x