  Accuracy in spreadsheet modelling systems can be reduced due to difficulties
with the inputs, the model itself, or the spreadsheet implementation of the
model. When the "true" outputs from the system are unknowable, accuracy is
evaluated subjectively. Less than perfect accuracy can be acceptable depending
on the purpose of the model, problems with inputs, or resource constraints.
Users build modelling systems iteratively, and choose to allocate limited
resources to the inputs, the model, the spreadsheet implementation, and to
employing the system for business analysis. When making these choices, users
can suffer from expectation bias and diagnosis bias. Existing research results
tend to focus on errors in the spreadsheet implementation. Because industry has
tolerance for system inaccuracy, errors in spreadsheet implementations may not
be a serious concern. Spreadsheet productivity may be of more interest.
