The phrase "assumptions less" is not correct and does not convey a clear meaning in written English.
It seems to be an incomplete expression and lacks context to determine its intended use.
Example: "In our analysis, we aim to be assumptions less to ensure objectivity."
Alternatives: "fewer assumptions" or "less reliance on assumptions".
This is a 'bias-correction' solution that has been shown to correct for remaining finite sample bias, while potentially also making violations of functional form assumptions less consequential [35, 36].
Alternative methods, including DEGSeq (Wang et al. 2010) and Myrna (Langmead et al. 2010), make overdispersion assumptions less consistent with our data, whereas other methods, including Cuffdiff, use an implementation specific to RNA-seq (Trapnell et al. 2013).
These are comedies of temporal and cultural collisions that work in both directions: are our own present-day assumptions any less volatile than the debates of the free love era?
New houses have far more performance data as part of their assessment for the code for sustainable homes, whereas the EPCs for existing houses have to make a lot of assumptions because less data is available.
For anyone even mildly fearful of confrontation, your evasive e-mail (about vacation assumptions, no less) would have been more than enough to communicate your feelings: we don't want your overgrown rugrats.
Further, multi-way sensitivity analyses that simultaneously varied these assumptions allowed less extreme (and more plausible) changes in individual variables while providing better visual fits to the published data.
These methods, and other methods whose assumptions are less clear, are reviewed elsewhere.
For example, under certain assumptions Do less:Emotional is cost effective relative to the MHI5 [ 29].
Other simplified models are readily available for impact projections related to non-infectious diseases, where transmission assumptions are less important.
On the other hand, the YPLL, PYPLL and WYPLL capture premature mortality as they are based on fewer assumptions and less data input.
The further away the data is from meeting these assumptions the less able the algorithms were at correctly identifying conserved genes.
