The American Statistical Association - the premier professional organization for statisticians - has come out against the p value. YES, there is an alternative, in fact there are several: start by reading Deming's "On Probability as a Basis for Action". It's free. My resource which started this whole thread also describes a powerful alternative graphs and probability.

I and my 'students' have solved hundreds if not thousands of very complex problems and never calculated a p value or performed a null hypothesis test. We have applied the same approaches to new product development - quite successfully. (caveat: we do have to report p values to our regulatory agency, but you know, it's the government...)

First understand that the whole null hypothesis a p value thing is a ritual: actions one take without thought just because it's always done that way.

The whole approach came about by mashing together the disjointed thoughts of two diametrically opposed statisticians: Fisher and Pearson.

What the p vlaue is NOT:

•The probability that there is no difference

•The probability that there is no effect due to the suspected cause

•The probability that the observed difference was produced by simple chance

•The probability of getting the observed difference if there really is no difference

The p value is the probability of results that conflict with the assumption of no difference by as much as or more than the observed results, IF all of the assumptions were true.

A low p value indicates the probability that

**at least one of the assumptions is not true**
•No real difference exists

•The data are homogeneous

•The selected distributional model is correct for the data

•The test statistic was correct for the data

•The data were random: the trials were not confounded or biased

You see the assumptions (requirements) matter. Simply saying the p value is less than .05 (a limit that Fisher pulled out of his back pocket with little to no thought at the dawn of statistics as a profession) without detailing the study design, including the sample sizes, and the underlying science is tantamount to scientific malpractice.

As a friend of mine once said: "Statistics without physics is gambling. Physics without statistics is psychics" The lack of appropriate study designs and relying the mythological p value is what results in coffee being bad for you today and good for you tomorrow.

a few other free articles to begin learning:

*“The Insignificance of Statistical Significance Testing”*, Johnson, Douglas H., Journal of Wildlife Management, Vol. 63, Issue 3, pp. 763-772, 1999

http://www.ecologia.ufrgs.br/~adrimelo/lm/apostilas/critic_to_p-value.pdf
*“The Case Against Statistical Significance Testing”*, Carver, Ronald P., Harvard Educational Review, Vol 48, Issue 3, pp 378-399, 1978

http://healthyinfluence.com/wordpress/wp-content/uploads/2015/04/Carver-SSD-1978.pdf
Cohen, Jacob, “The Earth is round (p<.05)”, American Psychologist, December 1994, Vol. 49, No. 12, pp. 997-1003

http://ist-socrates.berkeley.edu/~maccoun/PP279_Cohen1.pdf
Rozeboom, William W., “The Fallacy of the Null-Hypothesis Test”, Psychological Bulletin, 57, pp. 416-428, 1960

http://stats.org.uk/statistical-inference/Rozeboom1960.pdf
Wheeler, Donald, “

*Why We Keep Having Hundred Year Floods*”, Quality Digest, June 2013,

Why We Keep Having 100-Year Floods | Quality Digest
Wheeler, Donald, “

*The Secret Foundation of Statistical Analysis*”, Quality Digest, December 2015

The Secret Foundation of Statistical Inference | Quality Digest
Wheeler, Donald, “

*Statistics 101 and Data Analysis*”, Quality Digest, March 2016

Statistics 101 and Data Analysis: an Example | Quality Digest
A good book: Kida, Thomas, “Don’t Believe Everything You Think, Prometheus Books, 2006