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Wikipedia can be a great tool for learning and researching information. However, as with all reference works, not everything in Wikipedia is accurate, comprehensive, or unbiased. Many of the general rules of thumb for conducting research apply to Wikipedia, including:
Always be wary of any one single source (in any medium — web, print, television or radio), or of multiple works that derive from a single source.
Where articles have references to external sources (whether online or not) read the references and check whether they really do support what the article says.
In most academic institutions Wikipedia, like most encyclopedias and other tertiary sources, is unacceptable as a source for facts in a research paper.
Use the What Links Here link under Tools on the left hand of the page
Confirm that the reference support the statement in the Wikipedia article – trace it to the source
If a source is cited under References, copy the article title or book title into the library search box to find the original source. If it doesn’t show up, order it through Interlibrary Loan (articles) or EZBorrow (books)
Browse the See Also links at the foot of the Wikipedia article page
Metacognition & a Cognitive Toolkit: Detecting Baloney
Metacognition is simply thinking about thinking.
Because you think or believe something doesn’t mean it’s true.
In Carl Sagan’s book The Demon-Haunted World: Science as a Candle in the Dark he advocates skeptical inquiry and provides guidelines to use a “baloney detection kit”. He argues that it is not only for scientists, but a good approach for everyone to use whenever faced with ideas or claims.
Wherever possible there must be independent confirmation of the “facts.”
Encourage substantive debate on the evidence by knowledgeable proponents of all points of view.
Arguments from authority carry little weight — “authorities” have made mistakes in the past. They will do so again in the future. Perhaps a better way to say it is that in science there are no authorities; at most, there are experts.
Spin more than one hypothesis. If there’s something to be explained, think of all the different ways in which it could be explained. Then think of tests by which you might systematically disprove each of the alternatives. What survives, the hypothesis that resists disproof in this Darwinian selection among “multiple working hypotheses,” has a much better chance of being the right answer than if you had simply run with the first idea that caught your fancy.
Try not to get overly attached to a hypothesis just because it’s yours. It’s only a way station in the pursuit of knowledge. Ask yourself why you like the idea. Compare it fairly with the alternatives. See if you can find reasons for rejecting it. If you don’t, others will.
Quantify. If whatever it is you’re explaining has some measure, some numerical quantity attached to it, you’ll be much better able to discriminate among competing hypotheses. What is vague and qualitative is open to many explanations. Of course there are truths to be sought in the many qualitative issues we are obliged to confront, but finding them is more challenging.
If there’s a chain of argument, every link in the chain must work (including the premise) — not just most of them.
Occam’s Razor. This convenient rule-of-thumb urges us when faced with two hypotheses that explain the data equally well to choose the simpler.
Always ask whether the hypothesis can be, at least in principle, falsified. Propositions that are untestable, unfalsifiable are not worth much. Consider the grand idea that our Universe and everything in it is just an elementary particle — an electron, say — in a much bigger Cosmos. But if we can never acquire information from outside our Universe, is not the idea incapable of disproof? You must be able to check assertions out. Inveterate skeptics must be given the chance to follow your reasoning, to duplicate your experiments and see if they get the same result.