The analysis of skewness is an essential tool for decision-making since it can be used
as an indicator on risk assessment. It is well known that negative skewed distributions
lead to negative outcomes, while a positive skewness usually leads to good scenarios
and consequently minimizes risks. In this work the impact of skewness on risk analysis will be explored, considering data obtained from the product of two normally
distributed variables. In fact, modelling this product using a normal distribution is
not a correct approach once skewness in many cases is different from zero. By ignoring
this, the researcher will obtain a model understating the risk of highly skewed variables
and moreover, for too skewed variables most of common tests in parametric inference
cannot be used. In practice, the behaviour of the skewness considering the product of
two normal variables is explored as a function of the distributions parameters: mean,
variance and inverse of the coefficient variation. Using a measurement error model,
the consequences of skewness presence on risk analysis are evaluated by considering
several simulations and visualization tools using R software.info:eu-repo/semantics/publishedVersio
