There is a huge emphasis on hypothesis testing and the use of p values to dichotomize significant or non-significant findings throughout current literature. This, in turn, has detracted from more useful approaches to interpreting study results, such as estimation and confidence intervals (CI). In medical studies, investigators are usually interested in determining the size of difference of a measured outcome between groups, rather than a simple indication of whether or not it is statistically significant. Confidence intervals present a range of values, on the basis of the sample data, in which the population value for such a difference may lie.
Additionally, p values do not provide us with directionality of our results. For example, if we have a relative risk 90% CI of 1.4 to 1.9 in regards to development of lung cancer among patients that smoke, we can infer that the p value result is significant as 1 is not included within the confidence interval. In addition, we can state that patients that smoke are anywhere between 40 and 90% more likely to develop lung cancer in comparison to non-smoking patients.
On the other hand, if we have a relative risk 90% CI of 0.7 and 1.1 in regards to heart attacks among patients that eat chicken, we can infer that the p value result is NOT significant as 1 is included within the confidence interval. Additionally, we can state that we do not have evidence to conclude that patients that eat chicken are any more likely to have a heart attack than their non-chicken eating counterparts.
In other words, confidence intervals provide us with significance in the same way that p values do along with the added benefits of magnitude and direction! As such, confidence intervals > p values!