Statistical procedures and the justification of knowledge in psychological science.

Rosnow, Ralph L.; Rosenthal, Robert

doi:10.1037/0003-066x.44.10.1276

Cited by 776 publications

(400 citation statements)

References 72 publications

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“…Finally, this interpretation of P is flawed because the strength of evidence against the null hypothesis is a continuous function of the magnitude of P (ref. 41). npg statistics, such as the mean, to estimate features of the population from which the sample was drawn.…”

Section: Box 1 Power Analysis and Repeatabilitymentioning

confidence: 99%

The fickle P value generates irreproducible results

et al. 2015

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The reliability and reproducibility of science are under scrutiny. However, a major cause of this lack of repeatability is not being considered: the wide sample-to-sample variability in the P value. We explain why P is fickle to discourage the ill-informed practice of interpreting analyses based predominantly on this statistic.Reproducible research findings are a cornerstone of the scientific method, providing essential validation. There has been recent recognition, however, that the results of published research can be difficult to replicate 1-7 , an awareness epitomized by a series in Nature entitled "Challenges in irreproducible research" and by the Reproducibility Initiative, a project intended to identify and reward reproducible research (http:// validation.scienceexchange.com/#/ reproducibilityinitiative). In a recent meeting at the American Association for the Advancement of Science headquarters involving many of the major journals reporting biomedical science research, a common set of principles and guidelines was agreed upon for promoting transparency and reproducibility 8 . These discussions and initiatives all focused on a number of issues, including aspects of statistical reporting 9 , levels of statistical power (i.e., sufficient statistical capacity to find an effect; a 'statistically significant' finding) 10 and inclusion-exclusion criteria. Yet a fundamental problem inherent in standard statistical methods, one that is pervasively linked to the lack of reproducibility in research, remains to be considered: the wide sample-to-sample variability in the P value. This omission reflects a general lack of awareness about this crucial issue, and we address this matter here.Focusing on the P value during statistical analysis is an entrenched culture [11][12][13] . The P value is often used without the realization that in most cases the statistical power of a study is too low for P to assist the interpretation of the data (Box 1). Among the many and varied reasons for a fearful and hidebound approach to statistical practice, a lack of understanding is prominent 14 . A better understanding of why P is so unhelpful should encourage scientists to reduce their reliance on this misleading concept.Readers may know of the long-standing philosophical debate about the value and validity of null-hypothesis testing [15][16][17] . Although the P value formalizes null-hypothesis testing, this article will not revisit these issues. Rather, we concentrate on how P values themselves are misunderstood.Although statistical power is a central element in reliability 18 , it is often considered only when a test fails to demonstrate a real effect (such as a difference between groups): a 'false negative' result (see Box 2 for a glossary of statistical terms used in this article). Many scientists who are not statisticians do not realize that the power of a test is equally relevant when considering statistically significant results, that is, when the null hypothesis appears to be untenable. This is because the statistical power o...

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Section: Box 1 Power Analysis and Repeatabilitymentioning

confidence: 99%

The fickle P value generates irreproducible results

et al. 2015

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“…Contrast analysis was used which, in accordance with hypotheses, allowed focused comparisons between groups (see Rosnow & Rosenthal, 1989;Rosenthal & Rosnow, 1985).…”

Section: Methodsmentioning

confidence: 99%

Testing a Social-Cognitive Model of Intimate Abusiveness Among Substance-Dependent Males

Copenhaver

2000

The American Journal of Drug and Alcohol Abuse

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Throughout history, the human race has been characterized by the use of physical and emotional aggression by individuals, particularly males, in their intimate relationships. Intimate abusiveness is particularly common among substance dependent males. As a result of male intimate abusiveness, victims suffer a variety of problems ranging from emotional trauma to death due to physical injury. Despite increased attention to this problem, our understanding of the process leading to intimate abusiveness is far from comprehensive. The primary purpose of the present study was to expand our understanding of intimate abusiveness through the application of a social-cognitive model of intimate abusiveness among substance dependent males.Fifty-seven males from an inpatient substance abuse treatment program participated. Subjects completed questionnaires indicating their level of intimate abusiveness. In addition, they completed partner-related attribution measures as well as coping response measures indicating how they would interpret and handle five ambiguous vignettes involving their partner.It was hypothesized that violent men would attribute greater negative intent and responsibility to their partner and that they would choose to handle the ambiguous vignettes in less competent ways compared with non-violent men. Further, it was predicted that the association between intimate abusiveness and competency of coping responses would be mediated by attributions made about the partner. Results of the study generally supported predictions. The implications of the results are discussed as well as suggestions for future research.

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“…En las últimas décadas, un amplio número de autores han criticado el exceso y las interpretaciones erróneas de artículos que con este método han sido publicados en la investigación psicológica (Hubbard y Ryan, 2000). Por ejemplo, desde los años sesenta se tiene a Rozeboom (1960) y Bakan (1966; en los setenta a Carver (1978) y Meehl (1978; en los años ochenta Dar (1987), Rosnow y Rosenthal (1989), etc. En los años noventa la crítica se intensifica (e.g., Cohen, 1990Cohen, , 1994Schmidt, 1996;Thompson, 1999a).…”

Section: Controversia Del Nhstunclassified

Importancia del tamaño del efecto en el análisis de datos de investigación en psicología

Castillo-Blanco

Bravo

2015

Persona

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Statistical procedures and the justification of knowledge in psychological science.

Cited by 776 publications

References 72 publications

The fickle P value generates irreproducible results

The fickle P value generates irreproducible results

Testing a Social-Cognitive Model of Intimate Abusiveness Among Substance-Dependent Males

Importancia del tamaño del efecto en el análisis de datos de investigación en psicología

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