Hypothesis Warning: You are not logged in. Your IP address will be publicly visible if you make any edits. If you log in or create an account, your edits will be attributed to your username, along with other benefits.Anti-spam check. Do not fill this in! ===Statistical hypothesis testing=== {{Main|Statistical hypothesis testing}} When a possible [[correlation]] or similar relation between phenomena is investigated, such as whether a proposed remedy is effective in treating a disease, the hypothesis that a relation exists cannot be examined the same way one might examine a proposed new law of nature. In such an investigation, if the tested remedy shows no effect in a few cases, these do not necessarily falsify the hypothesis. Instead, [[statistical test]]s are used to determine how likely it is that the overall effect would be observed if the hypothesized relation does not exist. If that likelihood is sufficiently small (e.g., less than 1%), the existence of a relation may be assumed. Otherwise, any observed effect may be due to pure chance. In statistical hypothesis testing, two hypotheses are compared. These are called the [[null hypothesis]] and the [[alternative hypothesis]]. The null hypothesis is the hypothesis that states that there is no relation between the phenomena whose relation is under investigation, or at least not of the form given by the alternative hypothesis. The alternative hypothesis, as the name suggests, is the alternative to the null hypothesis: it states that there ''is'' some kind of relation. The alternative hypothesis may take several forms, depending on the nature of the hypothesized relation; in particular, it can be two-sided (for example: there is ''some'' effect, in a yet unknown direction) or one-sided (the direction of the hypothesized relation, positive or negative, is fixed in advance).<ref>[https://books.google.com/books?id=v-walRnRxWQC&dq=statistical+hypothesis+medical&pg=PA168 Altman. DG., ''Practical Statistics for Medical Research'', CRC Press, 1990, Section 8.5],</ref> Conventional significance levels for testing hypotheses (acceptable probabilities of wrongly rejecting a true null hypothesis) are .10, .05, and .01. The significance level for deciding whether the null hypothesis is rejected and the alternative hypothesis is accepted must be determined in advance, before the observations are collected or inspected. If these criteria are determined later, when the data to be tested are already known, the test is invalid.<ref name="Mellenbergh, 2008">Mellenbergh, G.J.(2008). Chapter 8: Research designs: Testing of research hypotheses. In [[H.J. AdΓ¨r]] & [[Gideon J. Mellenbergh|G.J. Mellenbergh]] (eds.) (with contributions by D.J. Hand), Advising on Research Methods: A consultant's companion (pp. 183β209). Huizen, The Netherlands: Johannes van Kessel Publishing</ref> The above procedure is actually dependent on the number of the participants (units or [[sample size]]) that are included in the study. For instance, to avoid having the sample size be too small to reject a null hypothesis, it is recommended that one specify a sufficient sample size from the beginning. It is advisable to define a small, medium and large effect size for each of a number of important statistical tests which are used to test the hypotheses.<ref>[https://books.google.com/books?id=v-walRnRxWQC&dq=statistical+hypothesis+medical&pg=PA168 Altman. DG., ''Practical Statistics for Medical Research'', CRC Press, 1990, Section 15.3],</ref> Summary: Please note that all contributions to Christianpedia may be edited, altered, or removed by other contributors. If you do not want your writing to be edited mercilessly, then do not submit it here. You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource (see Christianpedia:Copyrights for details). Do not submit copyrighted work without permission! Cancel Editing help (opens in new window) Discuss this page