Editing Statistical hypothesis test (section)

===Use and importance===
Statistics are helpful in analyzing most collections of data. This is equally true of hypothesis testing which can justify conclusions even when no scientific theory exists. In the Lady tasting tea example, it was "obvious" that no difference existed between (milk poured into tea) and (tea poured into milk). The data contradicted the "obvious".

Real world applications of hypothesis testing include:<ref name=larsen>{{cite book|author1=Richard J. Larsen |author2=Donna Fox Stroup |title=Statistics in the Real World: a book of examples|publisher=Macmillan|isbn=978-0023677205|year=1976}}</ref>
* Testing whether more men than women suffer from nightmares
* Establishing authorship of documents
* Evaluating the effect of the full moon on behavior
* Determining the range at which a bat can detect an insect by echo
* Deciding whether hospital carpeting results in more infections
* Selecting the best means to stop smoking
* Checking whether bumper stickers reflect car owner behavior
* Testing the claims of handwriting analysts

Statistical hypothesis testing plays an important role in the whole of statistics and in [[statistical inference]]. For example, Lehmann (1992) in a review of the fundamental paper by Neyman and Pearson (1933) says: "Nevertheless, despite their shortcomings, the new paradigm formulated in the 1933 paper, and the many developments carried out within its framework continue to play a central role in both the theory and practice of statistics and can be expected to do so in the foreseeable future".

Significance testing has been the favored statistical tool in some experimental social sciences (over 90% of articles in the ''Journal of Applied Psychology'' during the early 1990s).<ref name=hubbard>{{cite journal|author1=Hubbard, R. |author2=Parsa, A. R. |author3=Luthy, M. R. |title=The Spread of Statistical Significance Testing in Psychology: The Case of the Journal of Applied Psychology |journal=Theory and Psychology |volume=7 |pages=545–554 |year=1997|doi=10.1177/0959354397074006 |issue=4|s2cid=145576828 }}</ref> Other fields have favored the estimation of parameters (e.g. [[effect size]]). Significance testing is used as a substitute for the traditional comparison of predicted value and experimental result at the core of the [[scientific method]]. When theory is only capable of predicting the sign of a relationship, a directional (one-sided) hypothesis test can be configured so that only a statistically significant result supports theory. This form of theory appraisal is the most heavily criticized application of hypothesis testing.