  When performing data analysis, people often confront data sets containing
missing values. We conducted an empirical study to understand the effects of
visualizing those missing values on participants' decision-making processes
while performing a visual data exploration task. More specifically, our study
participants purchased a hypothetical portfolio of stocks based on a dataset
where some stocks had missing values for attributes such as PE ratio, beta, and
EPS. The experiment used scatterplots to communicate the stock data. For one
group of participants, stocks with missing values simply were not shown, while
the second group saw such stocks depicted with estimated values as points with
error bars. We measured participants' cognitive load involved in
decision-making with data with missing values. Our results indicate that their
decision-making workflow was different across two conditions.
