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Una de las dificultades más comunes que enfrentan los lectores de artículos del área de la biomedicina y epidemiología es la interpretación del término "significativo". El término "estadísticamente significativo" a menudo se malinterpreta como un resultado "clínicamente significativo". La confusión surge del hecho de que muchas personas equiparan "significativo" con su significado literal de "importante", sin embargo, la significación estadística cuantifica la probabilidad de que los resultados de un estudio se deban al azar, mientras que la significancia clínica refleja la importancia práctica o relevancia en el contexto de la atención médica o práctica clínica. Este artículo aborda la diferencia entre la significación estadística y la relevancia o importancia clínica en la interpretación de los resultados de una investigación biomédica.

One of the most common difficulties faced by readers of articles in the area of biomedicine and epidemiology is the interpretation of the term "significant". The term "statistically significant" is often misinterpreted as a "clinically significant" result. Confusion arises from the fact that many people equate "significant" with its literal meaning of "important," however, statistical significance quantifies the probability that the results of a study are due to chance, while clinical significance reflects the practical importance or relevance in the context of health care or clinical practice. This article addresses the difference between statistical significance and clinical relevance or importance in the interpretation of biomedical research results.

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El presente estudio constituye un trabajo trascendente en el área del conocimiento de la condición física y representa el resultado de investigaciones realizadas en la República de Cuba y en los Estados Unidos Mexicanos como respuesta a la solicitud de ambos países. Fue diseñado estadísticamente, para representar datos oficiales y altamente confiables, con el objetivo de conocer el estado de la condición física de las dos naciones y valorar así, el efecto de los programas de Educación Física que se aplican. Se contó con el apoyo de las organizaciones deportivas y de cultura física al conformar los estudios, cuidadosamente tratados en el diseño de muestra, para ello se contó con un equipo de estadísticos especialistas que tuvieron a su cargo el procesamiento de la información. Los datos de este estudio se consideraron limitados para la publicación y una vez desclasificados se dan conocer. Se utilizaron iguales metodologías en su aplicación, lo que resulta una información valiosa para el perfeccionamiento de los planes y programas que en el campo de la Licenciatura en Cultura Física y se brinda una información que, en su comparación, llama a la reflexión de los especialistas de Educación Física, para continuar el perfeccionamiento de estas especialidades, en general.

O presente estudo constitui um trabalho transcendental na área do conhecimento da aptidão física e representa o resultado de uma pesquisa realizada na República de Cuba e nos Estados Unidos Mexicanos em resposta à solicitação de ambos os países. Foi projetado estatisticamente para representar dados oficiais e altamente confiáveis, com o objetivo de conhecer o estado da aptidão física em ambos os países e, assim, avaliar o efeito dos programas de Educação Física aplicados. As organizações esportivas e de cultura física foram apoiadas na elaboração dos estudos, cuidadosamente tratadas no desenho da amostra, com a ajuda de uma equipe de estatísticos especializados que foram responsáveis pelo processamento das informações. Os dados deste estudo foram considerados limitados para publicação e, uma vez desclassificados, são tornados públicos. Foram utilizadas as mesmas metodologias em sua aplicação, o que resulta em informações valiosas para o aprimoramento dos planos e programas no campo da cultura física e fornece informações que, em sua comparação, exigem a reflexão dos especialistas em educação física, a fim de continuar o aprimoramento dessas especialidades em geral.

The present study constitutes a transcendent work in the area of knowledge of physical condition and represents the result of research carried out in the Republic of Cuba and in the United Mexican States in response to the request of both countries. It was designed statistically, to represent official and highly reliable data, with the objective of knowing the state of the physical condition of the two nations and thus evaluating the effect of the Physical Education programs that are applied. It was had the support of sports and physical culture organizations when forming the studies, carefully treated in the sample design, for this it was had a team of specialist statisticians who were in charge of processing the information. The data from this study was considered limited for publication and will be released once declassified. The same methodologies were used in its application, which is valuable information for the improvement of plans and programs in the field of the Bachelor's Degree in Physical Culture and information is provided that, in comparison, calls for reflection by specialists. of Physical Education, to continue the improvement of these specialties, in general.

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Null hypothesis significant testing (NHST) is the dominant statistical approach in the geriatric and rehabilitation fields. However, NHST is routinely misunderstood or misused. In this case, the findings from clinical trials would be taken as evidence of no effect, when in fact, a clinically relevant question may have a "non-significant" p-value. Conversely, findings are considered clinically relevant when significant differences are observed between groups. To assume that p-value is not an exclusive indicator of an association or the existence of an effect, researchers should be encouraged to report other statistical analysis approaches as Bayesian analysis and complementary statistical tools alongside the p-value (eg, effect size, confidence intervals, minimal clinically important difference, and magnitude-based inference) to improve interpretation of the findings of clinical trials by presenting a more efficient and comprehensive analysis. However, the focus on Bayesian analysis and secondary statistical analyses does not mean that NHST is less important. Only that, to observe a real intervention effect, researchers should use a combination of secondary statistical analyses in conjunction with NHST or Bayesian statistical analysis to reveal what p-values cannot show in the geriatric and rehabilitation studies (eg, the clinical importance of 1kg increase in handgrip strength in the intervention group of long-lived older adults compared to a control group). This paper provides potential insights for improving the interpretation of scientific data in rehabilitation and geriatric fields by utilizing Bayesian and secondary statistical analyses to better scrutinize the results of clinical trials where a p-value alone may not be appropriate to determine the efficacy of an intervention.

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Principal Component Analysis (PCA) is one of the most broadly used statistical methods for the ordination and dimensionality-reduction of multivariate datasets across many scientific disciplines. Trivial PCs can be estimated from data sets without any correlational structure among the original variables, and traditional criteria for selecting non-trivial PC axes are difficult to implement, partially subjective or based on ad hoc thresholds. PCAtest is an R package that implements permutation-based statistical tests to evaluate the overall significance of a PCA, the significance of each PC axis, and of contributions of each observed variable to the significant axes. Based on simulation and empirical results, I encourage R users to routinely apply PCAtest to test the significance of their PCA before proceeding with the direct interpretation of PC axes and/or the utilization of PC scores in subsequent evolutionary and ecological analyses.

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Psychological research often seeks general rules applying across individuals, an aim that is in tension with examining that which is unique to any individual. There are general statistical regularities across individuals' subjective self-report which enable much psychology and psychotherapy research to combine data from self-report questionnaire responses with statistical and psychometric methods to create a fundamental part of Cronbach and Meehl's foundational nomological networks of validity. However, these methods only apply when most participants answer the same questions on measures creating nomothetic data and this has led to a neglect of idiographic data. This paper reviews a method of analysis of idiographic data, of "rigorous idiography": the method of derangements. This is a remarkably simple statistical test of whether purely idiographic data convey reliable information. We show how the method appeared to become stuck in a bibliometric backwater but we expand on its potential for research and practise and hope it will be taken up and used correctly and more widely.

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Most randomized trials are designed and analyzed using frequentist statistical approaches such as null hypothesis testing and P values. Conceptually, P values are cumbersome to understand, as they provide evidence of data incompatibility with a null hypothesis (e.g., no clinical benefit) and not direct evidence of the alternative hypothesis (e.g., clinical benefit). This counterintuitive framework may contribute to the misinterpretation that the absence of evidence is equal to evidence of absence and may cause the discounting of potentially informative data. Bayesian methods provide an alternative, probabilistic interpretation of data. The reanalysis of completed trials using Bayesian methods is becoming increasingly common, particularly for trials with effect estimates that appear clinically significant despite P values above the traditional threshold of 0.05. Statistical inference using Bayesian methods produces a distribution of effect sizes that would be compatible with observed trial data, interpreted in the context of prior assumptions about an intervention (called "priors"). These priors are chosen by investigators to reflect existing beliefs and past empirical evidence regarding the effect of an intervention. By calculating the likelihood of clinical benefit, a Bayesian reanalysis can augment the interpretation of a trial. However, if priors are not defined a priori, there is a legitimate concern that priors could be constructed in a manner that produces biased results. Therefore, some standardization of priors for Bayesian reanalysis of clinical trials may be desirable for the critical care community. In this Critical Care Perspective, we discuss both frequentist and Bayesian approaches to clinical trial analysis, introduce a framework that researchers can use to select priors for a Bayesian reanalysis, and demonstrate how to apply our proposal by conducting a novel Bayesian trial reanalysis.

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BACKGROUND: There is a growing trend in using the "statistically significant" term in the scientific literature. However, harsh criticism of this concept motivated the recommendation to withdraw its use of scientific publications. We aimed to validate the support and the feasibility of adherence to this recommendation, among researchers having declared in favor of removing the statistical significance. METHODS: We surveyed signatories of an article published that defended this recommendation, to validate their opinion and ask them about how likely they will retire the concept of statistical significance. RESULTS: We obtained 151 responses which confirmed the support for the mentioned publication in aspects such as the adequate interpretation of the p-value, the degree of agreement, and the motivations to sign it. However, there was a wide distribution of answers about how likely are they to use the concept of "statistical significance" in future publications. About 42% declared being neutral, or that would likely use it again. We described arguments referred by several signatories and discussed aspects to be considered in the interpretation of research results. CONCLUSIONS: The responses obtained from a proportion of signatories validated their declared position against the use of statistical significance. However, even in this group, the full application of this recommendation does not seem feasible. The arguments related to the inappropriate use of statistical tests should promote more education among researchers and users of scientific evidence.

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Resumen ANTECEDENTES: El valor de p es el método más empleado para estimar la significación estadística de cualquier hallazgo; sin embargo, en los últimos años se ha intensificado su debate al respecto, debido a la baja credibilidad y reproducibilidad de diversos estudios. OBJETIVO: Describir el estado actual del concepto del valor de p y la significación estadística (prueba de significación de la hipótesis nula [por sus siglas en inglés: Null Hypothesis Significance Testing: NHST]), especificar los problemas más importantes y puntualizar las soluciones propuestas para la mejor utilización de los conceptos. METODOLOGÍA: Se llevó a cabo la búsqueda bibliográfica en MEDLINE y Google Scholar, con los términos: "NHST", "Statistical significance; P value" en idioma inglés y español, de 2018-2019, limitándose a la selección de artículos publicados entre 2005 y 2019, mediante la revisión de tipo narrativo con búsqueda manual; sobre todo estudios de metodología. RESULTADOS: La búsqueda global reportó 1411 artículos: 875 de PubMed y 536 de Google Scholar. Se excluyeron 817 por duplicación, 155 sin acceso completo y 414 ensayos clínicos (sin metodología estadística); los 25 restantes fueron el motivo del análisis. CONCLUSIONES: El concepto del valor de p no es simple, tiene varias falacias y malas interpretaciones que deben considerarse para evitarlas en lo posible. Se recomienda no usar el término "estadísticamente significativo" o "significativo", sustituir el umbral de 0.05 por 0.005, informar valores de p precisos y con IC95%, riesgo relativo, razón de momios, tamaño del efecto o potencia y métodos bayesianos.

Abstract BACKGROUND: The P value is the most widely used method of estimating the statistical significance of any finding, however, in recent years the debate over the P value has been increasingly intensified due to the low credibility and reproducibility of many studies. OBJECTIVE: To describe the current state of the concept of the value of P and the statistical significance (Null Hypothesis Significance Testing (NHST), specify the most important problems and point out the solutions proposed in the literature for their best use. METHODOLOGY: Search in MEDLINE and Google Scholar, with the terms: "NHST", "Statistical significance; P value "in English and Spanish, carried out from 2018-2019, limited to articles published from 2005 to 2019, and a narrative-type review with manual search. Articles on methodology were preferably selected. RESULTS: The global search yielded 1411 articles, 875 from PubMed and 536 from Google Scholar. 817 were excluded by duplication, 155 without full access, 414 from clinical trials, without statistical methodology. The 25 selected articles were the reason for the analysis. CONCLUSIONS: The concept of the value of P is not simple, and it has several fallacies and misinterpretations that must be taken into account to avoid them as much as possible. Recommendations: Do not use "statistically significant" or "significant", replace the threshold of 0.05 with 0.005, report accurate P values with 95% CI, relative risk, odds ratio, effect size or power, and Bayesian methods.

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Introducción: desde hace años, existe un debate sobre el uso de las pruebas estadísticas inferenciales en los reportes de resultados de investigación, se destaca la crítica al empleo de las pruebas de significación estadística y sus limitaciones. Objetivos: determinar la frecuencia de empleo de las pruebas de significación estadística (PSE) e intervalos de confianza (IC) por tipos de estudio publicado, cómo se reflejan los resultados de estas, la influencia del tamaño de la muestra, así comosu vinculación con las conclusiones. Resultados: en el periodo 2010 - 2015 de 150 artículos originales, 98 por ciento fueron descriptivos o explicativos y de ellos, el 95 por ciento emplea las PSE, solas o con IC. Predomina el uso de las PSE solas (69 por ciento de los trabajos). En el 25 por ciento se explica la selección del nivel de significación utilizado y el 53 por ciento de los estudios reflejan las cifras exactas de las pruebas realizadas. Solo el 15 por ciento menciona la influencia del tamaño de la muestra en relación con los resultados de las pruebas estadísticas. En las conclusiones, el 86 por ciento de los artículos se refieren adecuadamente a los objetivos del estudio. Conclusiones: predomina el uso de las PSE e IC, fundamentalmente de las PSE, más de la mitad de los trabajos mencionan los resultados precisos de las pruebas, la mayoría no argumenta la relación de estos resultados con el tamaño de la muestra y los autores elaboran las conclusiones de acuerdo con los objetivos planteados en el estudio(AU)

Introduction: For years there has been a debate about the use of inferential statistical tests in the reports of research results, highlighting the criticism to the use of tests of statistical significance and its limitations. Objectives: To determine the frequency of use of statistical significance tests (SST) and confidence intervals (CI) by published study types, how the results are reported, and the influence of sample size, as well as their relationship with the conclusions. Results: In the period 2010-2015 of 150 original articles, 98 percent were descriptive or explanatory and of them, 95 percent used SST alone or with CI. The use of SST alone (69 percent of the articles) predominates. In 25 percent the significance level selection is explained and 53 percent of the studies reflect the exact figures of the tests performed. Only 15 percent mentions the influence of sample size on the results of statistical tests. In the conclusions, 86 percent of the articles refer adequately to the objectives of the study. Conclusions: SST and CI use predominate, mainly SST, more than half of the studies mention the precise results of the tests, most do not argue the relation of these results to the sample size and the authors elaborate the conclusions in accordance with the objectives set out in the study(AU)

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