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  2. Confounding - Wikipedia

    en.wikipedia.org/wiki/Confounding

    A reduction in the potential for the occurrence and effect of confounding factors can be obtained by increasing the types and numbers of comparisons performed in an analysis. If measures or manipulations of core constructs are confounded (i.e. operational or procedural confounds exist), subgroup analysis may not reveal problems in the analysis.

  3. 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 ...

  4. 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.

  5. Lord's paradox - Wikipedia

    en.wikipedia.org/wiki/Lord's_paradox

    Lord's Paradox and associated analyses provide a powerful teaching tool to understand these fundamental statistical concepts. More directly, Lord's Paradox may have implications for both education and health policies that attempt to reward educators or hospitals for the improvements that their children/patients made under their care, which is ...

  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. Propensity score matching - Wikipedia

    en.wikipedia.org/wiki/Propensity_score_matching

    Choose appropriate confounders (variables hypothesized to be associated with both treatment and outcome) Obtain an estimation for the propensity score: predicted probability p or the log odds, log[p/(1 − p)]. 2. Match each participant to one or more nonparticipants on propensity score, using one of these methods: Nearest neighbor matching

  8. College Football Playoff first-round picks, predictions. Who ...

    www.aol.com/college-football-playoff-first-round...

    Ten years after the first College Football Playoff, we finally get the full experience of an expanded field of 12.Instead of just two semifinals and a championship game, we get three games spread ...

  9. Simpson's paradox - Wikipedia

    en.wikipedia.org/wiki/Simpson's_paradox

    In this example, the "lurking" variable (or confounding variable) causing the paradox is the size of the stones, which was not previously known to researchers to be important until its effects were included. [citation needed] Which treatment is considered better is determined by which success ratio (successes/total) is larger.

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