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  2. Confounding - Wikipedia

    en.wikipedia.org/wiki/Confounding

    Case-control studies assign confounders to both groups, cases and controls, equally. For example, if somebody wanted to study the cause of myocardial infarct and thinks that the age is a probable confounding variable, each 67-year-old infarct patient will be matched with a healthy 67-year-old "control" person.

  3. Blocking (statistics) - Wikipedia

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

    In the examples listed above, a nuisance variable is a variable that is not the primary focus of the study but can affect the outcomes of the experiment. [3] They are considered potential sources of variability that, if not controlled or accounted for, may confound the interpretation between the independent and dependent variables .

  4. Controlling for a variable - Wikipedia

    en.wikipedia.org/wiki/Controlling_for_a_variable

    For example, if an outdoor experiment were to be conducted to compare how different wing designs of a paper airplane (the independent variable) affect how far it can fly (the dependent variable), one would want to ensure that the experiment is conducted at times when the weather is the same, because one would not want weather to affect the ...

  5. Control variable - Wikipedia

    en.wikipedia.org/wiki/Control_variable

    A variable in an experiment which is held constant in order to assess the relationship between multiple variables [a], is a control variable. [2] [3] A control variable is an element that is not changed throughout an experiment because its unchanging state allows better understanding of the relationship between the other variables being tested.

  6. 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 ...

  7. Regression discontinuity design - Wikipedia

    en.wikipedia.org/wiki/Regression_discontinuity...

    In statistics, econometrics, political science, epidemiology, and related disciplines, a regression discontinuity design (RDD) is a quasi-experimental pretest–posttest design that aims to determine the causal effects of interventions by assigning a cutoff or threshold above or below which an intervention is assigned.

  8. Could changes be coming to Medicare, Medicaid with Dr. Oz ...

    www.aol.com/could-changes-coming-medicare...

    With President-elect Donald Trump's recent announcement of former surgeon-turned-TV host Dr. Mehmet Oz to lead the Centers for Medicare and Medicaid Services (CMS), questions are swirling about ...

  9. Lord's paradox - Wikipedia

    en.wikipedia.org/wiki/Lord's_paradox

    For example, we are not at all surprised when smallpox inoculation carries risks of fatal reaction, yet reduces overall mortality by eradicating smallpox. The direct effect (fatal reaction) in this case is negative for every stratum of the population, yet the total effect (on mortality) is positive for the population as a whole."