The basis of statistical inference is the formulation of a probability model to describe the data; an understanding of statistical inference requires some knowledge of the theory of probability.
In [ 24], Takemasa et al. applied gene ontology analysis (GO) to transcriptome and proteome data from human colorectal cancer samples, which led to a better understanding of functional inference at the physiological level and to potential drug targets.
Taken together, our study allows better understanding of the factors that determine the inference accuracy of both methods.
For instance, the teaching of non-parametric hypothesis tests will lead to a deeper understanding of statistical inference in general and specifically the interplay between a test statistic and a sampling distribution.
Crucially, the delicate balance of precision at different hierarchical levels has a profound effect on veridical inference – and may also offer a formal understanding of false inference in psychopathology (Adams, Stephan, Brown, Frith, & Friston, 2013; Fletcher & Frith, 2009; Friston, 2013).
The use of video scenes depicting human interactions could constitute an appropriate tool to enhance understanding of the characters' behavior and stimulate inferences on mental states.
A PCA on this section of the survey offers some understanding of the groupings of items and, by inference, the underlying structure of knowledge concerning assessment.
We understand a lot about how phylogenetic inferences work, but our understanding of how tree inference from patchy data works is still in its infancy.
This happens simply because the inference procedure requires an understanding of the time-evolution of the system in order to find these causal links.
