In fact, we care because the P value is just another way to describe the results of fixed level tests. Every so often, a call is made for a ban on significance tests. Papers and books are written. Conferences are held and their proceedings published. The main reason behind these movements this is that P values tell us nothing about the magnitudes of the effects that might lead to us to reject or fail to reject the null hypothesis.
Significance tests blur the distinction between statistical significance and practical importance .
It is possible for a difference of little practical importance to achieve a high degree of statistical significance. It is also possible for clinically important differences to be missed because an experiment lacks the power to detect them. However, significance tests provide a useful summary of the data and these concerns are easily remedied by supplementing significance tests with the appropriate confidence intervals for the effects of interest. When hypotheses of equal population means are tested, determining whether P is less than 0.05 is just another way of examining a confidence interval for the mean difference to see whether it excludes 0. This is a good time to revist the cholesterol studies presented during the discussion of confidence intervals.