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• W3123774562 abstract "La etiqueta p-hacking (pH) se refiere a un conjunto de prácticas oportunistas destinadas a hacer que sean significativos algunos valores p que deberían ser no significativos. Algunos han argumentado que debemos prevenir y luchar contra el pH por varias razones, especialmente debido a sus posibles efectos nocivos en la evaluación de los resultados de la investigación primaria y su síntesis meta-analítica. Nos focalizamos aquí en el efecto de un tipo específico de pH, centrado en estudios marginalmente significativos, en la estimación combinada del tamaño del efecto en el meta-análisis. Queremos saber cuánto deberíamos preocuparnos por su efecto de sesgo al evaluar los resultados de un meta-análisis. Hemos calculado el sesgo en una variedad de situaciones que parecen realistas en términos de prevalencia y de la definición operativa del pH. Los resultados muestran que en la mayoría de las situaciones analizadas el sesgo es inferior a una centésima (± 0.01), en términos de d o r. Para alcanzar un nivel de sesgo de cinco centésimas (± 0.05), tendría que haber una presencia masiva de este tipo de pH, lo que parece poco realista. Hay muchas buenas razones para luchar contra el pH, pero nuestra conclusión principal es que entre esas razones no se incluye que tenga un gran impacto en la estimación meta-analítica del tamaño del efecto. The label p-hacking (pH) refers to a set of opportunistic practices aimed at making statistically significant p values that should be non-significant. Some have argued that we should prevent and fight against pH for several reasons, especially because of its potential harmful effects on the assessment of both primary research results and their meta-analytical synthesis. We focus here on the effect of a specific type of pH, focused on marginally significant studies, on the combined estimation of effect size in meta-analysis. We want to know how much we should be concerned with its biasing effect when assessing the results of a meta-analysis. We have calculated the bias in a range of situations that seem realistic in terms of the prevalence and the operational definition of pH. The results show that in most of the situations analyzed the bias is less than one hundredth (± 0.01), in terms of d or r. To reach a level of bias of five-hundredths (± 0.05), there would have to be a massive presence of this type of pH, which seems rather unrealistic. There are many good reasons for fighting against pH, but our main conclusion is that among them is not that it has a big impact on the meta-analytical estimation of effect size." @default.
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• W3123774562 date "2021-01-01" @default.
• W3123774562 modified "2023-09-25" @default.
• W3123774562 title "The small impact of p-hacking marginally significant results on the meta-analytic estimation of effect size" @default.
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• W3123774562 doi "https://doi.org/10.6018/analesps.433051" @default.
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