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- W4311496585 abstract "Equivalence testing (ET) is a framework to determine if an effect is small enough to be considered meaningless, wherein meaningless is expressed as an equivalence interval (EI). Although traditional effect sizes (ESs) are important accompaniments to ET, these measures exclude information about the EI. Incorporating the EI is valuable for quantifying how far the effect is from the EI bounds. An ES measure we propose is the proportional distance (PD) from an observed effect to the smallest effect that would render it meaningful. We conducted two Monte Carlo simulations to evaluate the PD when applied to (1) mean differences and (2) correlations. The coverage rate and bias of the PD were excellent within the investigated conditions. We also applied the PD to two recent psychological studies. These applied examples revealed the beneficial properties of the PD, namely its ability to supply information above and beyond other statistical tests and ESs." @default.
- W4311496585 created "2022-12-26" @default.
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- W4311496585 creator A5082501018 @default.
- W4311496585 date "2022-12-13" @default.
- W4311496585 modified "2023-10-17" @default.
- W4311496585 title "Effect Sizes for Equivalence Testing: Incorporating the Equivalence Interval" @default.
- W4311496585 doi "https://doi.org/10.31234/osf.io/5buz9" @default.
- W4311496585 hasPublicationYear "2022" @default.
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