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  2. Confounding - Wikipedia

    en.wikipedia.org/wiki/Confounding

    In statistics terms, the make of the truck is the independent variable, the fuel economy (MPG) is the dependent variable and the amount of city driving is the confounding variable. To fix this study, we have several choices. One is to randomize the truck assignments so that A trucks and B Trucks end up with equal amounts of city and highway ...

  3. Controlling for a variable - Wikipedia

    en.wikipedia.org/wiki/Controlling_for_a_variable

    Instead, they must control for variables using statistics. Observational studies are used when controlled experiments may be unethical or impractical. For instance, if a researcher wished to study the effect of unemployment ( the independent variable ) on health ( the dependent variable ), it would be considered unethical by institutional ...

  4. Lord's paradox - Wikipedia

    en.wikipedia.org/wiki/Lord's_paradox

    This resolution of Lord’s Paradox answers both questions: (1) How to allow for preexisting differences between groups and (2) Why the data appear paradoxical. Pearl's do-calculus [6] further answers question (1) for any causal model assumed, including models with multiple unobserved confounders.

  5. Causal inference - Wikipedia

    en.wikipedia.org/wiki/Causal_inference

    Causal graph where the hidden confounders Z have an effect on the observable variables X, the outcome y and the choice of treatment t. Causal Inference has also been used for treatment effect estimation. Assuming a set of observable patient symptoms(X) caused by a set of hidden causes(Z) we can choose to give or not a treatment t.

  6. Simpson's paradox - Wikipedia

    en.wikipedia.org/wiki/Simpson's_paradox

    Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined. This result is often encountered in social-science and medical-science statistics, [ 1 ] [ 2 ] [ 3 ] and is particularly problematic when frequency data are unduly given ...

  7. Randomization - Wikipedia

    en.wikipedia.org/wiki/Randomization

    Randomization is a statistical process in which a random mechanism is employed to select a sample from a population or assign subjects to different groups. [1] [2] [3] The process is crucial in ensuring the random allocation of experimental units or treatment protocols, thereby minimizing selection bias and enhancing the statistical validity. [4]

  8. Leaving Thunder, Bucks off the NBA's Christmas game list has ...

    www.aol.com/leaving-thunder-bucks-off-nbas...

    Oklahoma City leads the Western Conference and has a MVP candidate in Shai Gilgeous-Alexander. Milwaukee has the NBA's leading scorer in Giannis Antetokounmpo. They were the teams that made their ...

  9. Rubin causal model - Wikipedia

    en.wikipedia.org/wiki/Rubin_causal_model

    Rubin defines a causal effect: Intuitively, the causal effect of one treatment, E, over another, C, for a particular unit and an interval of time from to is the difference between what would have happened at time if the unit had been exposed to E initiated at and what would have happened at if the unit had been exposed to C initiated at : 'If an hour ago I had taken two aspirins instead of ...