Sentence examples for generalization bias from inspiring English sources

Therefore, this study may have a generalization bias.

Possible methodological limitations include causal interpretation, generalization, recall bias, the use a measurement primarily used with adults, a potential regression towards the mean, and ceiling effects.

Normally, the results are prone to over-generalizations and biased theory-building.

Bayesian regularization (BR) constrains (shrinks) the magnitude of the networks weights, improves generalization and allows bias in parameter estimates towards values that are considered to be plausible, while reducing their variance; thus, there is a bias-variance trade-off [ 13].

DOI: http://dx.doi.org/10.7554/eLife.08351.012 Effects in preclinical studies can fail clinical generalization because of bias or random variation (internal validity), a mismatch between experimental operations and the clinical scenario modeled (construct validity), or idiosyncratic causal mediators in an experimental system (external validity) (Henderson et al., 2013).

As a descriptive study, it presents a relatively small number of patients that may bias generalizations regarding treatment, outcomes, and general manifestations of the diseases.

However, some features or limitations may reduce the evidence of the present studies, especially with respect to our secondary hypothesis: 1) It can be argued that the spatial restriction on a single German region and/or incomplete compliance of the participants contacted (16.8%) generated a sampling bias that prevents generalization of our results.

This phenomenon may relate to the balance between variance and bias of generalization error in statistics.

In her (1974), Hesse develops a justification for analogical reasoning along Carnapian lines by adopting what she calls the Clustering Postulate: the assumption that our epistemic probability function ought to have a built-in bias towards generalization.

Results suggest an interpretive bias and generalization of phobia-specific responses in specific phobia.

Hence, the performance of the proposed model has some advantages in comparison to both pure parametric models and pure non-parametric models in terms of learning bias and generalization variance especially on high-dimensional protein datasets.