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  2. 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.

  3. Dependent and independent variables - Wikipedia

    en.wikipedia.org/wiki/Dependent_and_independent...

    This effect is called confounding or omitted variable bias; in these situations, design changes and/or controlling for a variable statistical control is necessary. Extraneous variables are often classified into three types: Subject variables, which are the characteristics of the individuals being studied that might affect their actions.

  4. Moderation (statistics) - Wikipedia

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

    In statistics and regression analysis, moderation (also known as effect modification) occurs when the relationship between two variables depends on a third variable. The third variable is referred to as the moderator variable (or effect modifier ) or simply the moderator (or modifier ).

  5. Controlling for a variable - Wikipedia

    en.wikipedia.org/wiki/Controlling_for_a_variable

    The regression uses as independent variables not only the one or ones whose effects on the dependent variable are being studied, but also any potential confounding variables, thus avoiding omitted variable bias. "Confounding variables" in this context means other factors that not only influence the dependent variable (the outcome) but also ...

  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. Selection bias - Wikipedia

    en.wikipedia.org/wiki/Selection_bias

    Attrition bias is a kind of selection bias caused by attrition (loss of participants), [13] discounting trial subjects/tests that did not run to completion. It is closely related to the survivorship bias , where only the subjects that "survived" a process are included in the analysis or the failure bias , where only the subjects that "failed" a ...

  8. Bias–variance tradeoff - Wikipedia

    en.wikipedia.org/wiki/Bias–variance_tradeoff

    In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as we increase the number of tunable parameters in a model, it becomes more ...

  9. Scientific control - Wikipedia

    en.wikipedia.org/wiki/Scientific_control

    For example, if the fertilizer was spread by a tractor but no tractor was used on the unfertilized treatment, then the effect of the tractor needs to be controlled. A scientific control is an experiment or observation designed to minimize the effects of variables other than the independent variable (i.e. confounding variables). [1]

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