comparisons between values in different bins

In contrast to quantitative assessments, qualitative assessments typically employ a set of methods,
principles, or rules for assessing risk based on nonnumerical categories or levels (e.g., very low,
low, moderate, high, very high). This type of assessment supports communicating risk results to
decision makers. However, the range of values in qualitative assessments is comparatively small
in most cases, making the relative prioritization or comparison within the set of reported risks
difficult. Additionally, unless each value is very clearly defined or is characterized by meaningful
examples, different experts relying on their individual experiences could produce significantly
different assessment results. The repeatability and reproducibility of qualitative assessments are
increased by the annotation of assessed values (e.g., this value is high because of the following
reasons) and by the use of tables or other well-defined functions to combine qualitative values.
Finally, semi-quantitative assessments typically employ a set of methods, principles, or rules for
assessing risk that uses bins, scales, or representative numbers whose values and meanings are not
maintained in other contexts. This type of assessment can provide the benefits of quantitative and
qualitative assessments. The bins (e.g., 0-15, 16-35, 36-70, 71-85, 86-100) or scales (e.g., 1-10)
translate easily into qualitative terms that support risk communications for decision makers (e.g.,
a score of 95 can be interpreted as very high), while also allowing relative comparisons between
values in different bins or even within the same bin (e.g., the difference between risks scored 70
and 71 respectively is relatively insignificant, while the difference between risks scored 36 and 70
is relatively significant). The role of expert judgment in assigning values is more evident than in a
purely quantitative approach. Moreover, if the scales or sets of bins provide sufficient granularity,
relative prioritization among results is better supported than in a purely qualitative approach. As
in a quantitative approach, rigor is significantly lessened when subjective determinations are
buried within assessments, or when significant uncertainty surrounds a determination of value. As
with the nonnumeric categories or levels used in a well-founded qualitative approach, each bin or
range of values needs to be clearly defined and/or characterized by meaningful examples.