Editing Scientific method (section)

=== Relationship with statistics ===
When the scientific method employs statistics as a key part of its arsenal, there are mathematical and practical issues that can have a deleterious effect on the reliability of the output of scientific methods. This is described in a popular 2005 scientific paper "[[Why Most Published Research Findings Are False]]" by [[John Ioannidis]], which is considered foundational to the field of [[metascience]].<ref name="mostRwrong">{{Cite journal|title = Why Most Published Research Findings Are False|journal = PLOS Medicine|date = 2005-08-01|issn = 1549-1277|pmc = 1182327|pmid = 16060722|volume = 2|issue = 8|pages = e124|doi = 10.1371/journal.pmed.0020124|first = John P.A.|last = Ioannidis | doi-access=free }}</ref> Much research in metascience seeks to identify poor use of statistics and improve its use, an example being the [[misuse of p-values]].<ref>{{cite journal| url = https://pubs.asahq.org/anesthesiology/article/60/5/505/29253/Regarding-the-Misuse-of-t-Tests| title = Regarding the Misuse of ''t'' Tests| journal = Anesthesiology| date = May 1984| volume = 60| issue = 5| pages = 505| doi = 10.1097/00000542-198405000-00026| last1 = Schaefer| first1 = Carl F| pmid = 6711862| access-date = 2021-08-29 | archive-date = 2021-08-29 | archive-url = https://web.archive.org/web/20210829012031/https://pubs.asahq.org/anesthesiology/article/60/5/505/29253/Regarding-the-Misuse-of-t-Tests| url-status = live| doi-access = free}}</ref>

The points raised are both statistical and economical. Statistically, research findings are less likely to be true when studies are small and when there is significant flexibility in study design, definitions, outcomes, and analytical approaches. Economically, the reliability of findings decreases in fields with greater financial interests, biases, and a high level of competition among research teams. As a result, most research findings are considered false across various designs and scientific fields, particularly in modern biomedical research, which often operates in areas with very low pre- and post-study probabilities of yielding true findings. Nevertheless, despite these challenges, most new discoveries will continue to arise from hypothesis-generating research that begins with low or very low pre-study odds. This suggests that expanding the frontiers of knowledge will depend on investigating areas outside the mainstream, where the chances of success may initially appear slim.<ref name="mostRwrong"/>