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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. 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]

4. 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).

5. 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.

6. Matching (statistics) - Wikipedia

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

   By matching treated units to similar non-treated units, matching enables a comparison of outcomes among treated and non-treated units to estimate the effect of the treatment reducing bias due to confounding. [1] [2] [3] Propensity score matching, an early matching technique, was developed as part of the Rubin causal model, [4] but has been ...

7. NYT ‘Connections’ Hints and Answers Today, Wednesday, January 8

   www.aol.com/nyt-connections-hints-answers-today...

   Related: The 26 Funniest NYT Connections Game Memes You'll Appreciate if You Do This Daily Word Puzzle. Hints About Today's NYT Connections Categories on Wednesday, January 8. 1. These words ...

8. 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 ...

9. 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 ...