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1 In 2001, the American Psychology Association advised researchers to provide readers with information about statistical significance and, even more importantly, to provide information about the magnitude of the observed effect size and the confidence interval (1). A review of published papers in the fields of behavioral sciences, psychology, medical sciences, and nursing shows that only a few papers have reported on the calculation of the effect size or related interpretations (2). The null hypothesis significance testing (NHST) and the reports on the statistical significance value have long been considered as an incomplete measure for analysis (3). The statistical significance of the null hypothesis is made up of several factors: the actual effect, the size of the parameter in the population, the sample size, and the selected α level (4). The statistical level of significance or P-value refers to the probability of rejecting the null hypothesis provided that it is approved based on the observed data. In other words, the observed difference is not random (there is zero chance involved), so that the lower this value, the more one can trust the accuracy of the observed difference (5). One of the problems with tests of statistical significance is that they can only indicate that the observed relationship is not by chance. However, these tests cannot prevent the probability of the significance of minimal differences in large populations. Thus, one of the limitations of the level of significance is that it depends on the sample size in a way that increasing the sample size can lead to a decrease in the P-value. In fact, tests of statistical significance are used to reduce the type of Ι error. Meanwhile, such tests cannot protect the significance level of the research findings against the possibility of the type 2 error. The statistical significance (P-value) shows that there are some effects in the research, but it does not specify the value of the effects (6). Therefore, many critics argue that the indexes of effect size and confidence interval (CI) should also be considered in the reporting of the findings (7). The confidence interval provides an estimate of the range of the outcome measurement for a particular population. Research studies often use a 95% confidence level, which indicates that the researcher can strongly claim that the average score of the population is within the reported range. In general, the researcher determines a range and can make sure that the average score will be within that range, given 95% confidence (8). The effect size is defined as an objective and standard index which estimates the effect of correlation between two or more variables (7). Unlike NHST, the effect size is resistant against sample size, and therefore it can provide a more accurate measurement of the effect of an intervention or the extent of the relationship. By examining the magnitude of the effect size, the effect of the independent variable on the dependent variable can be measured. Accordingly, the effect size is the main finding in quantitative research. For example, based on Beck’s questionnaire, the scores of depression in two different imaginary groups were (3,5,7,8,6,4) and (5,7,11,9,7,8), respectively. Nursing Practice Today

The importance of reporting the effect size in quantitative studies