Abstract
Existing difficulties in interpretation of the results of statistical analysis have been repeatedly mentioned as one of the factors behind poor reproducibility of research findings in biomedical sciences followed by a series of publications presenting alternatives to improve the situation including a abandonment of p-values and significance testing. In this paper we briefly present the scope of the problem as well as Fischer and NeymanPearson approaches to hypothesis testing. Moreover, we present confidence intervals and effect size calculation as alternatives to dichotomization of the results as significant or not significant using a certain cut-off level. In addition, we summarize the pros and cons of suggestion to change the cut-off value from traditional 0.05 to 0.005. We also present a list of the most common misunderstandings of p-values discussed in international statistical literature. We conclude the paper with brief recommendations on careful interpretation of the results of statistical analysis to prevent misinterpretation and misuse of p-values in biomedical studies.