Statistical Reporting in Nursing Research: Addressing a Common Error in Reporting of <i>p</i> Values (<i>p</i> = .000)

Wu, Yanni; Zhou, Chunlan; Wang, Run; Ye, Xiaoling; Yang, Liu; Li, Chaixiu; Hu, Mingyu; Cong, Weilian

doi:10.1111/jnu.12595

Cited by 4 publications

(1 citation statement)

References 44 publications

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“…However, the redundant expression may mislead readers. In a study that examined 30 main nursing journals 8) , it was found that "p = 0.000," which is an error, was documented in 93.3% of the journals. Documenting p = 0.000 was likely due to the author wanting to strongly emphasize that the results obtained were significant.…”

Section: Notation Of P-valuesmentioning

confidence: 99%

Interpreting Results from Statistical Hypothesis Testing: Understanding the Appropriate P-value

Tsushima

2022

Phys Ther Res

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Clinical research based on epidemiological study designs requires a good understanding of statistical analysis. This paper discusses the common misconceptions of p-values so that researchers and readers of research papers will be able to properly present and understand the results of null hypothesis significance testing (NHST). The p-values calculated by NHST are categorized as three different types: "significant at p <0.05," "significant at p <0.01," or "not significant." If specified, they may be written as p = 0.124. The 95% confidence interval (CI) of the supplementary statistics is presented regardless of the p-value, and the range of the CI is observed and discussed to determine whether the results are clinically valid. The effect size (ES), which is a measure of the magnitude of the effect, is also referenced and discussed. However, the ES should not be overestimated. It is important to examine the actual descriptive statistics and consider them comprehensively as much as possible. A high detection power of 80% or more indicates that NHST with high accuracy was applied. However, even when it falls below 80%, it is important to consider the limitations of the study, because the results are not completely useless.

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Section: Notation Of P-valuesmentioning

confidence: 99%

Interpreting Results from Statistical Hypothesis Testing: Understanding the Appropriate P-value

Tsushima

2022

Phys Ther Res

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Investigating gender stereotypes in nursing/midwifery and engineering students in Ireland

Chatzi,

Kourousis

2024

International Journal of Educational Research Open

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Why we need to discuss statistical significance and p-values (again): Addressing the underlying issue of different probability interpretations and actionable recommendations

Holmberg

2024

Nordic Journal of Nursing Research

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Nursing practices must be evidence-based, yet grappling with statistics proves challenging, hampering the integration of research into patient care. Statistics, a branch of mathematics, is often viewed solely as a method across various disciplines, including nursing, focusing on analyzing and inferring from measurable data. As with other methods, it is easy to overlook underlying assumptions. Statistical significance and p-values are commonly used and misused concepts in statistics. While extensively discussed, these issues receive less attention in nursing. Papers addressing them often adopt practical approaches, overlooking the root cause: a misunderstanding of their meaning. To address this, I aim to clarify the fundamental concept underpinning these issues – probability – by defining its two main types, each carrying implications for statistical significance and p-values. In conclusion, I advocate for the continued use of p-values in nursing but emphasize the need for critical considerations in doing so.

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Statistical Reporting in Nursing Research: Addressing a Common Error in Reporting of p Values (p = .000)

Cited by 4 publications

References 44 publications

Interpreting Results from Statistical Hypothesis Testing: Understanding the Appropriate P-value

Interpreting Results from Statistical Hypothesis Testing: Understanding the Appropriate P-value

Investigating gender stereotypes in nursing/midwifery and engineering students in Ireland

Why we need to discuss statistical significance and p-values (again): Addressing the underlying issue of different probability interpretations and actionable recommendations

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