Editing Scientific method (section)

==Limits of method==

===Role of chance in discovery===
{{Main|Role of chance in scientific discoveries}}
[[File:Sample of penicillin mould presented by Alexander Fleming to Douglas Macleod, 1935 (9672239344).jpg|thumb|alt=left<!--#lefty anarchy-->|A famous example of discovery being stumbled upon was Alexander Fleming's [[Alexander Fleming#Discovery of penicillin|discovery of penicillin]]. One of his bacteria cultures got contaminated with mould in which surroundings the bacteria had died off; thereby the method of discovery was simply knowing what to look out for.<ref name="Tan & Tatsumura 2015">{{cite journal |last1=Tan |first1=Sy |last2=Tatsumura |first2=Y |date=July 2015 |title=Alexander Fleming (1881–1955): Discoverer of penicillin |journal=Singapore Medical Journal |volume=56 |issue=7 |pages=366–367 |doi=10.11622/smedj.2015105 |pmc=4520913 |pmid=26243971 |quote=An uncovered Petri dish sitting next to an open window became contaminated with mould spores. Fleming observed that the bacteria in proximity to the mould colonies were dying, as evidenced by the dissolving and clearing of the surrounding agar gel. He was able to isolate the mould and identified it as a member of the Penicillium genus.}}</ref>]]

Somewhere between 33% and 50% of all [[Scientific discovery|scientific discoveries]] are estimated to have been ''stumbled upon'', rather than sought out. This may explain why scientists so often express that they were lucky.<ref name=DunbarLuck>Dunbar, K., & Fugelsang, J. (2005). Causal Thinking in Science: How Scientists and Students Interpret the Unexpected. In M. E. Gorman, R.D. Tweney, D. Gooding & A. Kincannon (eds.), ''Scientific and Technical Thinking''. Mahwah, NJ: Lawrence Erlbaum Associates. pp. 57–79.</ref> Scientists themselves in the 19th and 20th century acknowledged the role of fortunate luck or serendipity in discoveries.<ref name="Serendip" /> [[Louis Pasteur]] is credited with the famous saying that "Luck favours the prepared mind", but some psychologists have begun to study what it means to be 'prepared for luck' in the scientific context. Research is showing that scientists are taught various heuristics that tend to harness chance and the unexpected.<ref name="DunbarLuck"/><ref name="Oliver, J.E. 1991">{{cite book |last=Oliver |first=J. E. |year=1991 |chapter=Ch. 2: Strategy for Discovery |title=The Incomplete Guide to the Art of Discovery |place=New York |publisher=Columbia University Press |isbn=9780231076203}}</ref> This is what [[Nassim Nicholas Taleb]] calls "Anti-fragility"; while some systems of investigation are fragile in the face of [[human error]], human bias, and randomness, the scientific method is more than resistant or tough – it actually benefits from such randomness in many ways (it is anti-fragile). Taleb believes that the more anti-fragile the system, the more it will flourish in the real world.<ref name=Anti-fragility>{{cite web |last=Taleb |first=Nassim N. |title=Antifragility — or — The Property Of Disorder-Loving Systems |url=http://www.edge.org/q2011/q11_3.html |url-status=dead |archive-date=2013-05-07 |archive-url=https://web.archive.org/web/20130507124322/http://www.edge.org/q2011/q11_3.html}}</ref>

{{anchor|startWithBugs}}Psychologist Kevin Dunbar says the process of discovery often starts with researchers finding bugs in their experiments. These unexpected results lead researchers to try to fix what they ''think'' is an error in their method. Eventually, the researcher decides the error is too persistent and systematic to be a coincidence. The highly controlled, cautious, and curious aspects of the scientific method are thus what make it well suited for identifying such persistent systematic errors. At this point, the researcher will begin to think of theoretical explanations for the error, often seeking the help of colleagues across different domains of expertise.<ref name="DunbarLuck"/><ref name="Oliver, J.E. 1991"/>

=== 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"/>

===Science of complex systems===
Science applied to complex systems can involve elements such as [[transdisciplinarity]], [[systems theory]], [[control theory#Open-loop and closed-loop (feedback) control|control theory]], and [[scientific modelling]].

In general, the scientific method may be difficult to apply stringently to diverse, interconnected systems and large data sets. In particular, practices used within [[Big data]], such as [[predictive analytics]], may be considered to be at odds with the scientific method,<ref>Anderson, Chris (2008) [http://www.uvm.edu/~pdodds/files/papers/others/2008/anderson2008a.pdf The End of Theory: The Data Deluge Makes the Scientific Method Obsolete] {{Webarchive|url=https://web.archive.org/web/20210502005844/http://www.uvm.edu/pdodds/files/papers/others/2008/anderson2008a.pdf |date=2021-05-02  }}. Wired Magazine 16.07</ref> as some of the data may have been stripped of the parameters which might be material in alternative hypotheses for an explanation; thus the stripped data would only serve to support the [[null hypothesis]] in the predictive analytics application. {{harvp| Fleck| 1979 |pp=38–50}} notes "a [[#startWithBugs|scientific discovery remains incomplete without considerations of the social practices]] that condition it".<ref name= bigDataCanBeIncomplete>[[Ludwik Fleck]] (1979) ''[https://worldpece.org/sites/default/files/artifacts/media/pdf/fleck_et_al._-_2008_-_genesis_and_development_of_a_scientific_fact.pdf Genesis and Development of a Scientific Fact] {{Webarchive|url=https://web.archive.org/web/20210826194119/https://worldpece.org/sites/default/files/artifacts/media/pdf/fleck_et_al._-_2008_-_genesis_and_development_of_a_scientific_fact.pdf |date=2021-08-26  }}''</ref>