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  2. Moderation (statistics) - Wikipedia

    en.wikipedia.org/wiki/Moderation_(statistics)

    The third variable is referred to as the moderator variable (or effect modifier) or simply the moderator (or modifier). [1] [2] The effect of a moderating variable is characterized statistically as an interaction; [1] that is, a categorical (e.g., sex, ethnicity, class) or continuous (e.g., age, level of reward) variable that is associated with ...

  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. Interaction (statistics) - Wikipedia

    en.wikipedia.org/wiki/Interaction_(statistics)

    Interaction effect of education and ideology on concern about sea level rise. In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the effect of one causal variable on an outcome depends on the state of a second causal variable (that is, when effects of the two causes are not additive).

  6. Controlling for a variable - Wikipedia

    en.wikipedia.org/wiki/Controlling_for_a_variable

    In other cases, controlling for a non-confounding variable may cause underestimation of the true causal effect of the explanatory variables on an outcome (e.g. when controlling for a mediator or its descendant). [2] [3] Counterfactual reasoning mitigates the influence of confounders without this drawback. [3]

  7. Mendelian randomization - Wikipedia

    en.wikipedia.org/wiki/Mendelian_randomization

    In epidemiology, Mendelian randomization (commonly abbreviated to MR) is a method using measured variation in genes to examine the causal effect of an exposure on an outcome. Under key assumptions (see below), the design reduces both reverse causation and confounding, which often substantially impede or mislead the interpretation of results ...

  8. Affect vs. Effect: What’s the Difference? - AOL

    www.aol.com/affect-vs-effect-difference...

    For instance, you could correctly say, “The effects of climate change can be felt worldwide” and “This medicine may have some side effects.” “Affect,” meanwhile, is a verb that means ...

  9. Simpson's paradox - Wikipedia

    en.wikipedia.org/wiki/Simpson's_paradox

    The lurking variable, stone size, has a large effect on the ratios; i.e., the success rate is more strongly influenced by the severity of the case than by the choice of treatment. Therefore, the group of patients with large stones using treatment A (group 3) does worse than the group with small stones, even if the latter used the inferior ...