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  2. Mill's methods - Wikipedia

    en.wikipedia.org/wiki/Mill's_Methods

    Symbolically, the method of concomitant variation can be represented as (with ± representing a shift): A B C occur together with x y z A± B C results in x± y z. ————————————————————— Therefore A and x are causally connected. Unlike the preceding four inductive methods, the method of concomitant ...

  3. Dunning–Kruger effect - Wikipedia

    en.wikipedia.org/wiki/Dunning–Kruger_effect

    [42] [43] Science communicator Jonathan Jarry makes the case that this effect is the only one shown in the original and subsequent papers. [44] Dunning has defended his findings, writing that purely statistical explanations often fail to consider key scholarly findings while adding that self-misjudgements are real regardless of their underlying ...

  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. Concomitant (statistics) - Wikipedia

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

    In statistics, the concept of a concomitant, also called the induced order statistic, arises when one sorts the members of a random sample according to corresponding values of another random sample. Let ( X i , Y i ), i = 1, . . ., n be a random sample from a bivariate distribution.

  6. 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).

  7. Failure mode and effects analysis - Wikipedia

    en.wikipedia.org/wiki/Failure_mode_and_effects...

    Failure mode and effects analysis (FMEA; often written with "failure modes" in plural) is the process of reviewing as many components, assemblies, and subsystems as possible to identify potential failure modes in a system and their causes and effects. For each component, the failure modes and their resulting effects on the rest of the system ...

  8. Tripod Beta - Wikipedia

    en.wikipedia.org/wiki/Tripod_Beta

    Tripod Beta is a methodology that can be conducted via pen and paper or using specialized software. [ 6 ] [ 7 ] The methodology combines a number of theories of accident causation into generating a single model (a 'Tripod tree') of an accident or incident, most notably the Swiss cheese model (barrier-based risk management) and human factors ...

  9. Statistical assumption - Wikipedia

    en.wikipedia.org/wiki/Statistical_assumption

    In the design-based approach, the model is taken to be known, and one of the goals is to ensure that the sample data are selected randomly enough for inference. Statistical assumptions can be put into two classes, depending upon which approach to inference is used. Model-based assumptions. These include the following three types: