Abstracts
Résumé
Cet article vise à expliciter la logique des tests d’hypothèses en recherche. Plusieurs études ont démontré une confusion quant à la signification des résultats de tests d’hypothèses. Cette confusion proviendrait en partie des points divergents entre les approches de Fisher, de Neyman et Pearson ainsi que de Bayes. L’article précise l’information fournie par le coefficient de signification, la taille de l’effet et la puissance et propose différents logiciels permettant l’analyse de la puissance et de la taille de l’effet.
Mots clés:
- tests d’hypothèses,
- signification statistique,
- taille de l’effet,
- puissance,
- inférence
Summary
This paper aims to explain the rationale of hypothesis testing in research. Many studies have shown some confusion as to the significance of hypothesis testing results. This confusion could partially come from discrepancies between the approaches of Fisher, Neyman and Pearson as well as Bayes. The article clarifies the information provided by the significance coefficient, effect size and power, and proposes various software to perform power and effect size analysis.
Key words:
- hypothesis testing,
- statistical significance,
- effect size,
- power,
- inference
Resumen
Este artículo tiene por objetivo explicitar la lógica de las pruebas de hipótesis en investigación. Varios estudios han demostrado una confusión en cuanto al significado de los resultados de las pruebas de hipótesis. Esta confusión tendría en parte por origen los puntos divergentes entre los enfoques de Fisher, de Neyman et Pearson así como de Bayes. El artículo precisa la información proporcionada por el coeficiente de significado, el tamaño del efecto y la potencia y propone distintos programas que permiten el análisis de la potencia y del tamaño del efecto.
Palabras claves:
- pruebas de hipótesis,
- significado estadístico,
- tamaño del efecto,
- potencia,
- inferencia
Appendices
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