pHacking.bib

@article{kerr_harking_1998,
	title = {{HARKing}: {Hypothesizing} {After} the {Results} are {Known}},
	volume = {2},
	issn = {1088-8683},
	shorttitle = {{HARKing}},
	url = {https://doi.org/10.1207/s15327957pspr0203_4},
	doi = {10.1207/s15327957pspr0203_4},
	abstract = {This article considers a practice in scientific communication termed HARKing (Hypothesizing After the Results are Known). HARKing is defined as presenting a post hoc hypothesis (i.e., one based on or informed by one's results) in one's research report as if it were, in fact, an a priori hypotheses. Several forms of HARKing are identified and survey data are presented that suggests that at least some forms of HARKing are widely practiced and widely seen as inappropriate. I identify several reasons why scientists might HARK. Then I discuss several reasons why scientists ought not to HARK. It is conceded that the question of whether HARKing's costs exceed its benefits is a complex one that ought to be addressed through research, open discussion, and debate. To help stimulate such discussion (and for those such as myself who suspect that HARKing's costs do exceed its benefits), I conclude the article with some suggestions for deterring HARKing.},
	language = {en},
	number = {3},
	urldate = {2021-12-11},
	journal = {Personality and Social Psychology Review},
	author = {Kerr, Norbert L.},
	month = aug,
	year = {1998},
	note = {Publisher: SAGE Publications Inc},
	pages = {196--217},
}

@misc{noauthor_false-positive_nodate,
	title = {False-{Positive} {Psychology}: {Undisclosed} {Flexibility} in {Data} {Collection} and {Analysis} {Allows} {Presenting} {Anything} as {Significant} - {Joseph} {P}. {Simmons}, {Leif} {D}. {Nelson}, {Uri} {Simonsohn}, 2011},
	url = {https://journals.sagepub.com/doi/full/10.1177/0956797611417632},
	urldate = {2021-12-11},
}

@article{lee_what_2020,
	title = {What is the proper way to apply the multiple comparison test?},
	volume = {73},
	doi = {10.4097/kja.d.18.00242.e1},
	journal = {Korean Journal of Anesthesiology},
	author = {Lee, Sangseok and Lee, Dong},
	month = dec,
	year = {2020},
	pages = {572--572},
	file = {Full Text:/home/xenia/.mozilla/firefox/r2x9kb5l.default/zotero/storage/YLZFBRZZ/Lee and Lee - 2020 - What is the proper way to apply the multiple compa.pdf:application/pdf},
}

@article{schwab_different_2020,
	title = {Different worlds {Confirmatory} versus exploratory research},
	volume = {17},
	issn = {1740-9713},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1740-9713.01369},
	doi = {10.1111/1740-9713.01369},
	abstract = {Simon Schwab and Leonhard Held explain the differences between confirmatory and exploratory research and the dangers of confusing the two concepts},
	language = {en},
	number = {2},
	urldate = {2021-12-11},
	journal = {Significance},
	author = {Schwab, Simon and Held, Leonhard},
	year = {2020},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1740-9713.01369},
	pages = {8--9},
	file = {Full Text PDF:/home/xenia/.mozilla/firefox/r2x9kb5l.default/zotero/storage/HN2EK3EV/Schwab and Held - 2020 - Different worlds Confirmatory versus exploratory r.pdf:application/pdf;Snapshot:/home/xenia/.mozilla/firefox/r2x9kb5l.default/zotero/storage/QIH9JH9H/1740-9713.html:text/html},
}

@article{sullivan_using_2012,
	title = {Using {Effect} {Size}—or {Why} the {P} {Value} {Is} {Not} {Enough}},
	volume = {4},
	issn = {1949-8349},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444174/},
	doi = {10.4300/JGME-D-12-00156.1},
	number = {3},
	urldate = {2021-12-11},
	journal = {Journal of Graduate Medical Education},
	author = {Sullivan, Gail M. and Feinn, Richard},
	month = sep,
	year = {2012},
	pmid = {23997866},
	pmcid = {PMC3444174},
	pages = {279--282},
	file = {PubMed Central Full Text PDF:/home/xenia/.mozilla/firefox/r2x9kb5l.default/zotero/storage/G5AJX8H7/Sullivan and Feinn - 2012 - Using Effect Size—or Why the P Value Is Not Enough.pdf:application/pdf},
}

@misc{campbell_medical_nodate,
	title = {Medical {Statistics}},
	urldate = {2021-12-11},
	author = {Campbell, Michael J.,  and Machin, David and Walters, Stephen J.},
	file = {Medical Statistics von Stephen J. Walters, David Machin, Michael J. Campbell. Bücher | Orell Füssli:/home/xenia/.mozilla/firefox/r2x9kb5l.default/zotero/storage/8LP42U5Z/A1000787002.html:text/html},
}

@misc{noauthor_understanding_nodate,
	title = {Understanding {The} {New} {Statistics} : {Geoff} {Cumming} :},
	urldate = {2021-12-11},
	file = {Understanding The New Statistics \: Geoff Cumming \: 9780415879682:/home/xenia/.mozilla/firefox/r2x9kb5l.default/zotero/storage/PALKIWWG/9780415879682.html:text/html},
}

@article{weissgerber_why_2018,
	title = {Why we need to report more than '{Data} were {Analyzed} by t-tests or {ANOVA}'},
	volume = {7},
	issn = {2050-084X},
	url = {https://doi.org/10.7554/eLife.36163},
	doi = {10.7554/eLife.36163},
	abstract = {Transparent reporting is essential for the critical evaluation of studies. However, the reporting of statistical methods for studies in the biomedical sciences is often limited. This systematic review examines the quality of reporting for two statistical tests, t-tests and ANOVA, for papers published in a selection of physiology journals in June 2017. Of the 328 original research articles examined, 277 (84.5\%) included an ANOVA or t-test or both. However, papers in our sample were routinely missing essential information about both types of tests: 213 papers (95\% of the papers that used ANOVA) did not contain the information needed to determine what type of ANOVA was performed, and 26.7\% of papers did not specify what post-hoc test was performed. Most papers also omitted the information needed to verify ANOVA results. Essential information about t-tests was also missing in many papers. We conclude by discussing measures that could be taken to improve the quality of reporting.},
	urldate = {2021-12-11},
	journal = {eLife},
	author = {Weissgerber, Tracey L and Garcia-Valencia, Oscar and Garovic, Vesna D and Milic, Natasa M and Winham, Stacey J},
	editor = {Teare, M Dawn and Rodgers, Peter A},
	month = dec,
	year = {2018},
	note = {Publisher: eLife Sciences Publications, Ltd},
	keywords = {analysis of variance, meta-research, statistics, systematic review, t-test, transparency},
	pages = {e36163},
	file = {Full Text PDF:/home/xenia/.mozilla/firefox/r2x9kb5l.default/zotero/storage/YPG97PFQ/Weissgerber et al. - 2018 - Why we need to report more than 'Data were Analyze.pdf:application/pdf},
}

@misc{statquest_with_josh_starmer_p-hacking_2020,
	title = {p-hacking: {What} it is and how to avoid it!},
	shorttitle = {p-hacking},
	url = {https://www.youtube.com/watch?v=HDCOUXE3HMM},
	abstract = {p-hacking is the misuse and abuse of p-values and results in being fooled by false positives. Some forms of p-hacking are obvious, but other are much more subtle. In this video, we talk about two forms of p-hacking and how to avoid them.

NOTE: This StatQuest assumes that you are already familiar with p-values, if not check out:
p-values: What they are and how to interpret them: https://youtu.be/vemZtEM63GY
How to calculate p-values: https://youtu.be/JQc3yx0-Q9E

Also, if you'd like to learn more about FDR, check out the 'Quest: https://youtu.be/K8LQSvtjcEo

⭐ NOTE: When I code, I use Kite, a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I love it! https://www.kite.com/get-kite/?utm\_me...

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https://statquest.org/video-index/

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0:00 Awesome song and introduction
0:28 Simple example of p-hacking
2:21 p-hacking defined
2:38 Multiple testing problem explained
7:29 Using FDR to compensate for multiple testing
8:52 A subtle form of p-hacking
11:26 Power analysis to prevent p-hacking
12:12 Summary of concepts

\#statquest \#pvalue},
	urldate = {2021-12-11},
	author = {{StatQuest with Josh Starmer}},
	month = may,
	year = {2020},
}

@misc{geoff_cumming_intro_2013,
	title = {Intro {Statistics} 9 {Dance} of the p {Values}},
	url = {https://www.youtube.com/watch?v=5OL1RqHrZQ8},
	abstract = {I use pictures from the ESCI software to give a brief, easy account of the Dance of the p Values. The simulation illustrates how enormously and disastrously variable the p value is, simply because of sampling variability. Never trust a p value! Use estimation, not NHST! Most researchers don't appreciate just how unreliable a p value is!
This is Video 9 of 10 that introduce some basic statistical ideas underlying 'The New Statistics', meaning estimation (effect sizes, confidence intervals, and meta-analysis). The New Statistics provide a much better way to draw conclusions from data than the traditional null hypothesis significance testing (NHST). More information about my book and the ESCI software is at: www dot thenewstatistics dot com},
	urldate = {2021-12-11},
	author = {{Geoff Cumming}},
	month = sep,
	year = {2013},
}

@misc{pernet_hacking_2017,
	type = {presentation},
	title = {Hacking, {HARKing} and {SHARKING} your research: a tutorial},
	shorttitle = {Hacking, {HARKing} and {SHARKING} your research},
	url = {https://figshare.com/articles/presentation/Hacking_HARKing_and_SHARKING_your_research_a_tutorial/5451067/1},
	abstract = {A sarcastic tutorial on p-hacking, harking, sharking in behavioural and brain imaging sciences, with a discussion on cognitive biases that make us, humans, make mistakes in conducting research.},
	language = {en},
	urldate = {2021-12-11},
	author = {Pernet, Cyril},
	month = sep,
	year = {2017},
	doi = {10.6084/m9.figshare.5451067.v1},
	note = {Publisher: figshare},
	file = {Full Text PDF:/home/xenia/.mozilla/firefox/r2x9kb5l.default/zotero/storage/5VG4FYZN/Pernet - 2017 - Hacking, HARKing and SHARKING your research a tut.pdf:application/pdf;Snapshot:/home/xenia/.mozilla/firefox/r2x9kb5l.default/zotero/storage/KXAR6BI9/5451067.html:text/html},
}

@article{weissgerber_beyond_2015,
	title = {Beyond {Bar} and {Line} {Graphs}: {Time} for a {New} {Data} {Presentation} {Paradigm}},
	volume = {13},
	issn = {1545-7885},
	shorttitle = {Beyond {Bar} and {Line} {Graphs}},
	url = {https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128},
	doi = {10.1371/journal.pbio.1002128},
	abstract = {Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.},
	language = {en},
	number = {4},
	urldate = {2021-12-13},
	journal = {PLOS Biology},
	author = {Weissgerber, Tracey L. and Milic, Natasa M. and Winham, Stacey J. and Garovic, Vesna D.},
	month = apr,
	year = {2015},
	note = {Publisher: Public Library of Science},
	keywords = {Graphs, Medical journals, Parametric analysis, Prisms, Scientists, Statistical data, Statistical distributions, Systematic reviews},
	pages = {e1002128},
	file = {Full Text PDF:/home/xenia/.mozilla/firefox/r2x9kb5l.default/zotero/storage/PK6JFHUE/Weissgerber et al. - 2015 - Beyond Bar and Line Graphs Time for a New Data Pr.pdf:application/pdf;Snapshot:/home/xenia/.mozilla/firefox/r2x9kb5l.default/zotero/storage/N2NM88KX/article.html:text/html},
}