Editing Null hypothesis (section)

==History of statistical tests==
{{main|Statistical hypothesis testing#Origins and early controversy}}
The history of the null and alternative hypotheses has much to do with the history of statistical tests.<ref name=Gigerenzer>{{cite book|title=The Empire of Chance: How Probability Changed Science and Everyday Life|last=Gigerenzer|first=Gerd|author2=Zeno Swijtink |author3=Theodore Porter |author4=Lorraine Daston |author5=John Beatty |author6=Lorenz Kruger |year=1989|publisher=Cambridge University Press|chapter=Part 3: The Inference Experts|isbn=978-0-521-39838-1|pages=70–122}}</ref><ref>{{cite book | last = Lehmann | first = E. L. | title = Fisher, Neyman, and the creation of classical statistics | publisher = Springer | location = New York | year = 2011 | isbn = 978-1441994998 }}</ref>

* Before 1925: There are occasional transient traces of statistical tests in past centuries, which were [[Statistical hypothesis testing#Early choices of null hypothesis|early examples]] of null hypotheses. In the late 19th century statistical significance was defined. In the early 20th century important [[probability distributions]] were defined. [[William Sealy Gosset|Gossett]] and [[Karl Pearson|Pearson]] worked on specific cases of significance testing.
* 1925: [[Ronald Fisher|Fisher]] published the first edition of ''[[Statistical Methods for Research Workers]],'' which defined the statistical significance test and made it a mainstream method of analysis for much of experimental science. The text was devoid of proofs and weak on explanations, but was filled with real examples. It placed statistical practice in the sciences well in advance of published statistical theory.
* 1933: In a series of papers (published over a decade starting in 1928) [[Jerzy Neyman|Neyman]] & [[Egon Pearson|Pearson]] defined the statistical hypothesis test as a proposed improvement on Fisher's test. The papers provided much of the terminology for statistical tests including ''alternative hypothesis'' and H<sub>0</sub> as a hypothesis to be tested using observational data (with H<sub>1</sub>, H<sub>2</sub>... as alternatives).<ref name="Neyman 289–337">{{cite journal|last1=Neyman|first1=J|title=On the Problem of the most Efficient Tests of Statistical Hypotheses|journal=[[Philosophical Transactions of the Royal Society A]]|date=1 January 1933|volume=231|issue=694–706|pages=289–337|doi=10.1098/rsta.1933.0009|last2=Pearson|first2=E. S.|bibcode=1933RSPTA.231..289N|doi-access=free}}</ref> 
* 1935: Fisher published the first edition of the book ''[[The Design of Experiments]]'' which introduced the null hypothesis<ref>{{cite web
 | last = Aldrich | first = John
 | title = Earliest Known Uses of Some of the Words of Probability & Statistics
 | url = http://www.leidenuniv.nl/fsw/verduin/stathist/1stword.htm
 | access-date = 30 June 2014 }} Last update 12 March 2003.  From Jeff Miller.</ref> (by example rather than by definition) and carefully explained the rationale for significance tests in the context of the interpretation of experimental results.
* Fisher and Neyman quarreled over the relative merits of their competing formulations until Fisher's death in 1962. Career changes and World War II ended the partnership of Neyman and Pearson. The formulations were merged by relatively anonymous textbook writers, experimenters (journal editors) and mathematical statisticians without input from either Fisher or Neyman.<ref name=Gigerenzer/> The subject today combines much of the terminology and explanatory power of Neyman & Pearson with the scientific philosophy and calculations provided by Fisher. Whether statistical testing is properly one subject or two remains a source of disagreement.<ref>{{cite journal|last=Lehmann|first=E. L.|title=The Fisher, Neyman-Pearson Theories of Testing Hypotheses: One Theory or Two?|journal=Journal of the American Statistical Association|volume=88|issue=424|pages=1242–1249|date=December 1993|doi=10.1080/01621459.1993.10476404}}</ref> Sample of two: One text refers to the subject as hypothesis testing (with no mention of significance testing in the index) while another says significance testing (with a section on inference as a decision). Fisher developed significance testing as a flexible tool for researchers to weigh their evidence. Instead testing has become institutionalized. Statistical significance has become a rigidly defined and enforced criterion for the publication of experimental results in many scientific journals. In some fields significance testing has become the dominant and nearly exclusive form of statistical analysis. As a consequence the limitations of the tests have been exhaustively studied. Books have been filled with the collected [[Statistical hypothesis testing#Criticism|criticism of significance testing]].