<P> Disagreements over one - tailed tests flow from the philosophy of science . While Fisher was willing to ignore the unlikely case of the Lady guessing all cups of tea incorrectly (which may have been appropriate for the circumstances), medicine believes that a proposed treatment that kills patients is significant in every sense and should be reported and perhaps explained . Poor statistical reporting practices have contributed to disagreements over one - tailed tests . Statistical significance resulting from two - tailed tests is insensitive to the sign of the relationship; Reporting significance alone is inadequate . "The treatment has an effect" is the uninformative result of a two - tailed test . "The treatment has a beneficial effect" is the more informative result of a one - tailed test . "The treatment has an effect, reducing the average length of hospitalization by 1.5 days" is the most informative report, combining a two - tailed significance test result with a numeric estimate of the relationship between treatment and effect . Explicitly reporting a numeric result eliminates a philosophical advantage of a one - tailed test . An underlying issue is the appropriate form of an experimental science without numeric predictive theories: A model of numeric results is more informative than a model of effect signs (positive, negative or unknown) which is more informative than a model of simple significance (non-zero or unknown); in the absence of numeric theory signs may suffice . </P> <P> The history of the null and alternative hypotheses is embedded in the history of statistical tests . </P> <Ul> <Li> Before 1925: There are occasional transient traces of statistical tests for centuries in the past, which provide early examples of null hypotheses . In the late 19th century statistical significance was defined . In the early 20th century important probability distributions were defined . Gossett and Pearson worked on specific cases of significance testing . </Li> <Li> 1925: 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 it was filled with real examples . It placed statistical practice in the sciences well in advance of published statistical theory . </Li> <Li> 1933: In a series of papers (published over a decade starting in 1928) Neyman & 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 as a hypothesis to be tested using observational data (with H, H...as alternatives). Neyman did not use the term null hypothesis in later writings about his method . </Li> <Li> 1935: Fisher published the first edition of the book "The Design of Experiments" which introduced the null hypothesis (by example rather than by definition) and carefully explained the rationale for significance tests in the context of the interpretation of experimental results; see The Design of Experiments #Quotations regarding the null hypothesis . </Li> <Li> Following: 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 the principals . 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 . 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 criticism of significance testing . </Li> </Ul> <Li> Before 1925: There are occasional transient traces of statistical tests for centuries in the past, which provide early examples of null hypotheses . In the late 19th century statistical significance was defined . In the early 20th century important probability distributions were defined . Gossett and Pearson worked on specific cases of significance testing . </Li>

What is the null hypothesis for the experimental test described in question 15