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  2. Correlation does not imply causation - Wikipedia

    en.wikipedia.org/wiki/Correlation_does_not_imply...

    The phrase "correlation does not imply causation" refers to the inability to legitimately deduce a cause-and-effect relationship between two events or variables solely on the basis of an observed association or correlation between them.

  3. Confounding - Wikipedia

    en.wikipedia.org/wiki/Confounding

    Confounding is defined in terms of the data generating model. Let X be some independent variable, and Y some dependent variable.To estimate the effect of X on Y, the statistician must suppress the effects of extraneous variables that influence both X and Y.

  4. Spurious relationship - Wikipedia

    en.wikipedia.org/wiki/Spurious_relationship

    Graphical model: Whereas a mediator is a factor in the causal chain (top), a confounder is a spurious factor incorrectly implying causation (bottom). In statistics, a spurious relationship or spurious correlation [1] [2] is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third ...

  5. List of fallacies - Wikipedia

    en.wikipedia.org/wiki/List_of_fallacies

    Cum hoc ergo propter hoc (Latin for 'with this, therefore because of this'; correlation implies causation; faulty cause/effect, coincidental correlation, correlation without causation) – a faulty assumption that, because there is a correlation between two variables, one caused the other. [57]

  6. Simpson's paradox - Wikipedia

    en.wikipedia.org/wiki/Simpson's_paradox

    One of the best-known examples of Simpson's paradox comes from a study of gender bias among graduate school admissions to University of California, Berkeley.The admission figures for the fall of 1973 showed that men applying were more likely than women to be admitted, and the difference was so large that it was unlikely to be due to chance.

  7. Causal analysis - Wikipedia

    en.wikipedia.org/wiki/Causal_analysis

    Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. [1] Typically it involves establishing four elements: correlation, sequence in time (that is, causes must occur before their proposed effect), a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the ...

  8. The Book of Why - Wikipedia

    en.wikipedia.org/wiki/The_Book_of_Why

    The chapter finishes with two examples, used to introduce the use of instrumental variables to estimate causal relationships. The first is John Snow's discovery that cholera is caused by unsanitary water supplies. The second is the relationship between cholesterol levels and likelihood of a heart attack.

  9. Magical thinking - Wikipedia

    en.wikipedia.org/wiki/Magical_thinking

    These beliefs can cause a person to experience an irrational fear of performing certain acts or having certain thoughts because of an assumed correlation between doing so and threatening calamities. [1] In psychiatry, magical thinking defines false beliefs about the capability of thoughts, actions or words to cause or prevent undesirable events ...

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