Many people are often confused about the meaning of sensitivity analysis and typically finds it hard to understand how the values are calculated. This is further compounded by the problem of equivocation of the same term across different industries. For many people, this number was not even in their analysis of the modeling.

Sensitivity analysis in different context has different meanings. In generic modeling, sensitivity analysis will refer to the amount of change to the model given a change to a particular factor. The term is also more commonly used in linear programming and operation research. In cases such data mining, sensitivity analysis less commonly encountered.

The most common way to create a sensitivity analysis is to run linear regression with an independent variable and a dependent one. Doing this allows one to measure the change to one variable given a change to the other one. However, there are a few problems to note. Such models are typically less than useful when the R-square is less than 0.8. This is because the explanatory power is very weak and may not model the process properly. Another problem commonly encountered was that there were manipulation of the variables to increase the R-squared value. Such manipulation is rare but usually increases the R-squared value to extremely high levels.

Sensitivity analysis should always be done in a cautious manner to ensure its usefulness.