Heritable variation with small effects on quantitative traits is widely believed to be the usual substrate of most evolutionary change.
For instance, we don't disagree with Gardner's defense of Fisher's modeling, but our point about Fisher's emphasis on adaptive genetic variation with small effects is that Fisher, like most mainstream evolutionary biology from the 1930s to the present, prematurely dismissed saltationist and developmental explanations for evolutionary change.
The results are more difficult to interpret, but, in essence, symmetric variation with small dispersion in multiple parameters still led to subtle differences in model predictions between homogenous and heterogeneous populations, whereas high dispersion generally led to larger discrepancies between both types of populations.
In randomized controlled trials spurious differences in the distributions of background factors can emerge due to random variation especially with small sample sizes.
Due to intra- and interindividual variation along with small sample sizes, data derived experimentally tends to encompass innate variability.
Smoothing decreases the random variation associated with small case numbers and small populations, enabling disease gradients or holes to be observed that may not be apparent with raw data (20, 26, 27).
The fact that GWAS are looking to identify common genetic variation associated with small effect sizes requires large study samples (at least 1000 cases and 1000 controls), which often leads to consortia coming together to pool data.
Spatial smoothing is used to reduce random variation associated with small populations and enables observations of gradients or holes of disease incidence that may not apparent from direct observation of raw data [ 2, 10, 11].
Spatial smoothing is used to reduce random variation associated with small populations and enables observation of gradients or holes in disease incidence that may not be apparent from direct observation of the raw data.
