BACKGROUND: An understanding of p-values and confidence intervals is necessary for the evaluation of scientific articles. This article will inform the reader of the meaning and interpretation of these two statistical concepts. METHODS: The uses of these two statistical concepts and the differences between them are discussed on the basis of a selective literature search concerning the methods employed in scientific articles. RESULTS/ CONCLUSIONS: P-values in scientific studies are used to determine whether a null hypothesis formulated before the performance of the study is to be accepted or rejected. In exploratory studies, p-values enable the recognition of any statistically noteworthy findings. Confidence intervals provide information about a range in which the true value lies with a certain degree of probability, as well as about the direction and strength of the demonstrated effect. This enables conclusions to be drawn about the statistical plausibility and clinical relevance of the study findings. It is often useful for both statistical measures to be reported in scientific articles, because they provide complementary types of information.
BACKGROUND: An understanding of p-values and confidence intervals is necessary for the evaluation of scientific articles. This article will inform the reader of the meaning and interpretation of these two statistical concepts. METHODS: The uses of these two statistical concepts and the differences between them are discussed on the basis of a selective literature search concerning the methods employed in scientific articles. RESULTS/ CONCLUSIONS: P-values in scientific studies are used to determine whether a null hypothesis formulated before the performance of the study is to be accepted or rejected. In exploratory studies, p-values enable the recognition of any statistically noteworthy findings. Confidence intervals provide information about a range in which the true value lies with a certain degree of probability, as well as about the direction and strength of the demonstrated effect. This enables conclusions to be drawn about the statistical plausibility and clinical relevance of the study findings. It is often useful for both statistical measures to be reported in scientific articles, because they provide complementary types of information.
Authors: Elisabeth Diehl; Sandra Rieger; Stephan Letzel; Anja Schablon; Albert Nienhaus; Luis Carlos Escobar Pinzon; Pavel Dietz Journal: PLoS One Date: 2021-01-22 Impact factor: 3.240
Authors: Mathew D Rand; Daria Vorojeikina; Edwin van Wijngaarden; Brian P Jackson; Thomas Scrimale; Grazyna Zareba; Tanzy M Love; Gary J Myers; Gene E Watson Journal: Toxicol Sci Date: 2015-11-15 Impact factor: 4.849