I started this work because over the years I have seen the results of too many studies misinterpreted by authors and clinicians alike (ref 2-5 below).
In fact others have criticized the use of these old methods. For example p-values have been widely criticized (refs 6-13 below). Others have also criticized confidence intervals as they do not always convey the precise implications of published data (ref 14), and are subject to abuse and misinterpretation (ref 15). In addition, these statistical methods do not help clinical decision-making: for example: how likely is it that a benefit is shown with a new treatment, or how confident are we that a clinically relevant benefit exists?

We can improve on these methods using confidence levels, clinical significance curves, and confidence contours.

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11. International Committee of Medical Journal Editors. Uniform requirements for manuscripts submitted to biomedical journals. N Engl J Med 1997; 336: 309–15.

12. Simon R. Confidence intervals for reporting results of clinical trials. Ann Intern Med 1986; 105: 429–35.

13. Braitman LE. Confidence intervals extract clinically useful information from data. Ann Intern Med 1988; 108: 296–8.

14. Goodman SN. Toward evidence-based medical statistics. 1: The P value fallacy. Ann Intern Med 1999; 130: 995–1004.

15. DeRouen TA. Statistical testing and confidence intervals (Letter). Am J Public Health 1987;77:237.