Abstract
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. 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. 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.
Highlights
An understanding of p-values and confidence intervals is necessary for the evaluation of scientific articles
If the p-value is < 0.05, the chance that this is the case is under 5%
Very restricted statements about effect strength are possible on the basis of p-values
Summary
An understanding of p-values and confidence intervals is necessary for the evaluation of scientific articles. There is no significant difference between the mean systolic blood pressures in the groups if the dispersion is high (figure 1c), the confidence level is high (figure 1e) or the sample size is small (figure 1g), as the value zero is contained in the confidence interval Point estimates, such as the arithmetic mean, the difference between two means or the odds ratio, provide the best approximation to the true value, they do not provide any information about how exact they are. > When a point estimate is used (for example, difference in means, relative risk), an attempt is made to draw conclusions about the situation in the target population on the basis of only a single value for the sample Even though this figure is the best possible approximation to the true value, it is not very probable that the values are exactly the same. The investigator should be more interested in the size of the difference in therapeutic effect between two treatment groups in clinical studies, as this is what is important for successful treatment, rather than whether the result is statistically significant or not [18]
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