Moving nursing beyond <i><b>p</b></i> &lt; 0.05

Hayat, Matthew J.; Staggs, Vincent S.; Schwartz, Todd A.; Higgins, Melinda; Azuero, Andres; Budhathoki, Chakra; Chandrasekhar, Rameela; Cook, Paul F.; Cramer, Emily; Dietrich, Mary S.; Garnier‐Villarreal, Mauricio; Hanlon, Alexandra L.; He, Jia; Hu, Jinxiang; Kim, Myoung Jin; Mueller, Martina; Nolan, Joseph R.; Perkhounkova, Yelena; Rothers, Janet; Schluck, Glenna; Su, Xiaogang; Templin, Thomas; Weaver, Michael T.; Yang, Qing; Ye, Sangbeak

doi:10.1002/nur.21954

Cited by 18 publications

(6 citation statements)

References 5 publications

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“…In accordance with current recommendations, frequencies, percentages, means, standard deviations, and 95 percent confidence intervals (CIs) were used to describe data (Hayat et al, 2019; Spurlock, 2017). To equalize the range and variability of all outcomes scores, as well as understand and compare the magnitude of students’ learning gains or losses, all outcome scores were standardized to a 0 to 100 percent scale.…”

Section: Methodsmentioning

confidence: 99%

Graduate Student Outcomes Following Critical Care Simulation: An Italian Pilot Study

Dante¹,

Bruni²,

Cerra³

et al. 2021

Nurs Educ Perspect

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High-fidelity simulation provides nursing students with the opportunity to learn and achieve competence in a safe context. The aim of the study was to assess learning outcomes following multiple exposures to high-fidelity simulation sessions. The sample consisted of 18 graduate students enrolled in a critical care nursing course. A four-hour high-fidelity simulation experience was conducted, with a four-hour retraining one month after. Group performance, self-efficacy, self-confidence, and satisfaction improved after multiple exposures to high-fidelity simulation. High-fidelity simulation is a valid adjunct to nursing education in the short term and may improve learning when offered at multiple time points.

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Section: Methodsmentioning

confidence: 99%

Graduate Student Outcomes Following Critical Care Simulation: An Italian Pilot Study

Dante¹,

Bruni²,

Cerra³

et al. 2021

Nurs Educ Perspect

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“…This American Statistical Association's statement on p-values 28 shows that the problems are widely recognised by statisticians. Numerous fields of research have recognised the need to move beyond significance testing, such as medicine, 29 specific medical subdisciplines, 30,31 nursing, 32 psychology, 33 neuroscience, 34 pharmacy, 35 toxicology, 36 anthropology 37 and animal research. 38 Is there a publication that explains confidence intervals from first principles?…”

Section: Question Resourcesmentioning

confidence: 99%

Statistical inference through estimation: Recommendations from the International Society of Physiotherapy Journal Editors

Elkins

Pinto

Verhagen

et al. 2022

Brazilian Journal of Physical Therapy

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“… 4 Again, an exhaustive review is intractable. For some examples, see Amrhein, Greenland, and McShane (2019a), Bernard (2019), Bijak (2019), Bresee (2019), Curran-Everett (2019), Davidson (2019), De Koning and Noordhof (2019), Dirnagl (2019), Harrington et al (2019), Harvey and Brinkhof (2019), Hayat et al (2019), Lowe (2019), Marshall (2019), McShane et al (2019), Morken (2019), Nguyen, Rivadeneira, and Civitelli (2019), Pickler (2019), O’Connor (2019), Parsons et al (2019), Staggs (2019), Carlsson and Gönen (2020), Charlesworth and Pandit (2020), Curran-Everett (2020), Johnson et al (2020), Knottnerus and Tugwell (2020), Marshall and Hughes (2020), Maula and Stam (2020), Michel, Murphy, and Motulsky (2020), Price, Bethune, and Massey (2020), Santibáñez, García-Rivero, and Barreiro (2020), Van Witteloostuijn (2020), Heckelei et al (2021), Imbens (2021), Putt (2021), Robinson and Haviland (2021), Tijssen (2021), Amrhein and Greenland (2022), Butler (2022), Elkins et al (2022), Filippini and Vinceti (2022), Greenland, Mansournia, and Joffe (2022), Bonovas and Piovani (2023), Fingerhut (2023), Hassler (2023), Montero, Hedeland, and Balgoma (2023), and Verykouki and Nakas (2023).…”

mentioning

confidence: 99%

“Statistical Significance” and Statistical Reporting: Moving Beyond Binary

McShane,

Bradlow,

Lynch

et al. 2024

Journal of Marketing

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Null hypothesis significance testing (NHST) is the default approach to statistical analysis and reporting in marketing and the biomedical and social sciences more broadly. Despite its default role, NHST has long been criticized by both statisticians and applied researchers including those within marketing. Therefore, the authors propose a major transition in statistical analysis and reporting. Specifically, they propose moving beyond binary: abandoning NHST as the default approach to statistical analysis and reporting. To facilitate this, they briefly review some of the principal problems associated with NHST. They next discuss some principles that they believe should underlie statistical analysis and reporting. They then use these principles to motivate some guidelines for statistical analysis and reporting. They next provide some examples that illustrate statistical analysis and reporting that adheres to their principles and guidelines. They conclude with a brief discussion.

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Moving nursing beyond p < 0.05

Cited by 18 publications

References 5 publications

Graduate Student Outcomes Following Critical Care Simulation: An Italian Pilot Study

Graduate Student Outcomes Following Critical Care Simulation: An Italian Pilot Study

Statistical inference through estimation: Recommendations from the International Society of Physiotherapy Journal Editors

“Statistical Significance” and Statistical Reporting: Moving Beyond Binary

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