A Taxonomy of Number Entry Error

Abstract

People are prone to errors in many aspects of life, including when entering numbers. The effects of these errors can be disastrous, for example when an incorrect number is entered when programming a medical infusion pump or when entering financial information into a system. Designing better systems may help to prevent these errors however, in order to do this we need to understand far more about the types of errors being made, and their causes. Unfortunately, there are very few documented examples of number entry errors and thus many of the studies conducted so far rely upon modelled, not real world data. This paper reports a study that was designed to elicit number entry errors and the subsequent process of creating a taxonomy of errors from the information gathered. A total of 345 errors were gathered. A method for classifying the errors using 13 codes is proposed-this is a significantly higher figure than previously suggested, showing that currently we underestimate the true variety of such errors. These codes are then organised into a taxonomy similar to that of Zhang et al (2004). We show how this taxonomy can be used to guide future research into number entry errors by suggesting experimental conditions needed to provoke certain errors. The taxonomy may also be used during the initial stages of design to help the designer understand the categories of errors that users are most likely to make and thus design accordingly.

Increasing Accuracy by Decreasing Presentation Quality in Transcription Tasks

“…By pressing the Enter key the current displayed number was saved and at the same time the display was cleared. The display itself provided participants to enter a 5-digit number with two digits after a separating decimal point as previously gathered numbers from infusion pump logs confirmed the validity of this number range [19]. There were three versions of the interface used in the study.…”

“…Previous studies have shown that human errors are the source of more than 50% of incidents in most domains and in aviation can be as high as 90% [20]. In healthcare the process of studying errors is only now emerging [19]. Recent studies have shown that people make transcription errors frequently and many systems do little to detect and prevent errors [18].…”

Employing Number-Based Graphical Representations to Enhance the Effects of Visual Check on Entry Error Detection

“…This makes the decimal point prominent in the GR (see Fig. 2c), as decimal point related errors are serious and common (Oladimeji et al, 2011;Wiseman et al, 2011).…”

“…Visual checking is a widely adopted method due to its advantages of high user preference and short execution time (Barchard and Pace, 2011; Barchard and Verenikina, 2011;Wiseman et al, 2011). Checking simply requires to compare entries with instructions to determine if they match.…”

QOC-E: A Mediating Representation to Support the Development of Shared Rationale and Integration of Human Factors Advice

“…There are multiple parameters that could be required during a typical programming sequence including the Volume to Be Infused (VTBI), the rate of infusion and/or the time taken to complete an infusion (Furniss, Blandford, Rajkomar, Vincent, & Mayer, 2011). Figure 4 outlines an illustrative and simplified representation of design decisions corresponding to the specification of number entry input (for a more in detailed treatment see (Campos & Harrison, 2011;Cauchi et al, 2011;Masci et al, 2011;Oladimeji, Thimbleby, & Cox, 2011;Thimbleby & Cairns, 2010;Wiseman, Cairns, & Cox, 2011)).…”