Statistics in ophthalmology revisited: the (effect) size matters.

Abstract

The association of biomedical sciences with mathematics is inextricable and undeniable. It should be considered that gaining confidence or strong belief within the framework of a scientific experiment - in the 'compulsory' marriage with statistics - does not mean total and undisturbed certainty. The null hypothesis significance testing (NHST) has been the kernel of statistical data analysis for a long period. Medicine is making use of it in a very wide range. The main problem in NHST arises out of a dichotomic perception of reality. Scientific reasoning was biased in the aftermath of the uncritical, almost dogmatic, trust in a level of statistical significance, namely a p-value, defined as 'the dictating paradigm of p-value'. The level of statistical significance (p-value) is not completely plausible and objective; it cannot be equated with the nature of a phenomenon or population characteristics. The p-value refers directly to a formulated hypothesis only, that is the probability of observing a given outcome under the condition posited by a specific null hypothesis and given a specific model of the distribution of outcomes under the null hypothesis. Experts claim that unjustified conclusions and guidelines have become the bane of many scientific journals. Investigators should be able to interpret study results in a broad context and look at them critically. Statistics indeed is a leaven to further scientific discussion, not its conclusion. It is not easy to abandon the p-value. Lots of researchers recommended an ecumenical approach to the statistical analysis of research data, for example analyses of the same results using a variety of statistical procedures. Many of them express the view that a threshold level of statistical significance should be lower than p<0.05. To enhance the plausibility and true scientific value of research works, investigators should also take into consideration the effect size along with its confidence limits. The effect size is a kind of added value and should be determined a priori. Account must be taken of their origin, conditioning (medical knowledge), as well as specific implications in clinical practice. The effect size makes possible to compare results of various research works. It is argued that to the rescue for modern science can come research works that bear the stamp of reproducibility, for example studies conducted in several independent centres, based on coherent and meticulous methodology, or data from various centres typed in one spotless big database and submitted to rigorous and deliberate multilevel statistical analyses.