Editing Multicollinearity (section)

==== Acceptance ====
While the above strategies work in some situations, estimates using advanced techniques may still produce large standard errors. In such cases, the correct response to multicollinearity is to "do nothing".<ref name=":3" /> The [[Scientific method|scientific process]] often involves [[Null result|null]] or inconclusive results; not every experiment will be "successful" in the sense of decisively confirmation of the researcher's original hypothesis.

Edward Leamer notes that "The solution to the weak evidence problem is more and better data. Within the confines of the given data set there is nothing that can be done about weak evidence".<ref name=":5" /> Leamer notes that "bad" regression results that are often misattributed to multicollinearity instead indicate the researcher has chosen an unrealistic [[prior probability]] (generally the [[flat prior]] used in [[Ordinary least squares|OLS]]).<ref name=":5"/>

[[Damodar N. Gujarati|Damodar Gujarati]] writes that "we should rightly accept [our data] are sometimes not very informative about parameters of interest".<ref name=":3" /> [[Olivier Blanchard]] quips that "multicollinearity is God's will, not a problem with [[Ordinary least squares|OLS]]";<ref name=":2" /> in other words, when working with [[observational data]], researchers cannot "fix" multicollinearity, only accept it.