If there is ANY statistical calculation that holds true value for researchers and clinicians on a day-to-day basis, it is the 95% confidence interval wrapped around the findings of inferential analyses. Statistics is not an exact mathematical science as far as other exact mathematical sciences go, measurement error is inherent when attempting to measure for anything related to human beings, and FEW tried and true causal effects have been proven scientifically. Statistics' strength as a mathematical science is in its ability to build confidence intervals around findings to put them into a relative context.  

Also, 95% confidence intervals act as the primary inference associated with unadjusted odds ratios, relative risk, hazard ratios, and adjusted odds ratios. If the confidence interval crosses over 1.0, there is a non-significant effect. Wide 95% confidence intervals are indicative of small sample sizes and lead to decreased precision of the effect. Constricted or narrow 95% confidence intervals reflect increased precision and consistency of a treatment effect.

In essence, p-values should not be what people get excited about when it comes to statistical analyses. The interpretation of your findings within the context of the subsequent population means, odds, risk, hazard, and 95% confidence intervals IS the real "meat" of applied statistics.