Accuracy in Spreadsheet Modelling Systems

TL;DR

This paper discusses the factors affecting accuracy in spreadsheet modeling systems, emphasizing the subjective nature of accuracy evaluation, user biases, and the focus on spreadsheet implementation errors versus overall productivity.

Contribution

It highlights the importance of considering broader aspects like productivity and user biases, beyond just implementation errors, in assessing spreadsheet modeling accuracy.

Findings

Accuracy is often subjective and context-dependent.

Errors in implementation may be less critical than productivity issues.

User biases influence resource allocation and error diagnosis.

Abstract

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.