  Visual statistical inference is a way to determine significance of patterns
found while exploring data. It is dependent on the evaluation of a lineup, of a
data plot among a sample of null plots, by human observers. Each individual is
different in their cognitive psychology and judiciousness, which can affect the
visual inference. The usual way to estimate the effectiveness of a statistical
test is its power. The estimate of power of a lineup can be controlled by
combining evaluations from multiple observers. Factors that may also affect the
power of visual inference are the observers' demographics, visual skills, and
experience, the sample of null plots taken from the null distribution, the
position of the data plot in the lineup, and the signal strength in the data.
This paper examines these factors. Results from multiple visual inference
studies using Amazon's Mechanical Turk are examined to provide an assessment of
these. The experiments suggest that individual skills vary substantially, but
demographics do not have a huge effect on performance. There is evidence that a
learning effect exists but only in that observers get faster with repeated
evaluations, but not more often correct. The placement of data plot in the
lineup does not affect the inference.
