The phrase "assumption may result" is correct and usable in written English.
It can be used when discussing the potential outcomes or consequences of a particular assumption in a given context.
Example: "If we proceed without verifying the data, the assumption may result in significant errors in our analysis."
Alternatives: "assumption could lead to" or "assumption might cause".
The analysis presented in this paper shows that this assumption may result in a conservative solution rather than an optimum.
But this assumption may result in large difference in the stress distribution of the membrane and the cable, especially when anti-symmetric loads applied on the membrane surface.
This assumption may result in structural mismatches along the synthesized video.
The assumption may result in under-estimation of hydraulic fracture size, and therefore RTA results may be optimistic.
This assumption may result in overestimation of diffusivities of the species for a given particle size (using the experimentally observed pressure relaxation as a measure of the gas uptake) but it should not affect our general conclusions about factors governing the rates of the processes.
Mortality rates are typically 10 15 times higher in HIV infected people, so this assumption may result in a small underestimate of the true incidence This incidence is a risk measure, which will closely approximate the rate since the number of annual incident cases is small relative to the whole population size.
Apparently well-founded assumptions may result in large approximation errors [ 5].
Here, depending on the technique used, violation of model assumptions may result in increased type I error, power loss, or biased parameter estimates.
However, the quantitative chemical analysis relies on several factors; therefore, standard assumptions may result in enormous errors in quantification [ 19, 20].
Using the analytical methods to estimate SRV parameters, it assumes a homogeneous single-porosity system and a fully stimulated pore volume with 100%% productive shale (optimistic assumption), which may result in under-estimation of hydraulic fracture size.
Farrington and Whitaker [ 17] have shown that the case-series method may be robust to failure of this assumption and may result in small negative bias.
