This approach could lead to incorrect effect estimation due to possible common method variance/ bias [ 33– 33].
If this assumption were incorrect, the estimated effects would be biased, most probably towards the null hypothesis of no effect.
But that first impression would be incorrect, because each effect cited above – like the employer mandate or the health care cost reduction – is a tax effect, and simple arithmetic is all that is needed to determine the direction of the combined effect of all of the tax-like provisions.
Establishing measurement equivalence is important because lack of measurement equivalence may lead to incorrect estimates of effects in research [ 19].
Lack of measurement equivalence may lead to incorrect estimates of effects in research and decision making [ 19].
Its potentially adverse effects, incorrect identification of differentially expressed transcripts and overly-optimistic significance tests, can be fully avoided, however, by the sound application of recently established theory and models for data analysis.
Most studies using the PREE must assume interval level scaling or that parametric statistics are so robust that this will not affect results, since most rely on parametric statistics to make their conclusions, lack of interval level scaling or differential item functioning may lead to incorrect estimation of effects or false study conclusions.
For the number incorrect, the fixed effect for time was significant (F[2,133.9] = 5.7, p < 0.01), but the effects for randomization and time by randomization were non-significant.
The Monte Carlo error can be so large that it overwhelms the bias of the underlying numerical method completely; in this case all of the numerical results are, in effect, incorrect, as they are random fluctuations.
Contamination and incorrect interpretations can result in snowball effects of erroneous secondary research [152].
