Error Rate

TL;DR

• A metric that quantifies mistakes relative to possible outcomes, usually reported as a percentage.
• Used to assess the accuracy of systems or processes and to track improvements.
• Reducing error rate can improve patient outcomes, financial decisions, and product reliability.

Definition

Error rate is a measure of the accuracy of a system or process. It is typically expressed as a percentage and represents the number of errors made divided by the total number of opportunities for error. In other words, it is the number of mistakes or incorrect decisions made by a system or process, relative to the total number of possible outcomes.

$$\text{Error rate} = \frac{\text{Number of errors}}{\text{Total number of opportunities for error}} \times 100\%$$

Explanation

Error rate quantifies how often a system or process produces incorrect results. Because it is usually reported as a percentage, it provides a normalized measure that can be compared across different contexts or systems. Measuring error rate helps identify shortcomings in decision-making processes and indicates where interventions or improvements are needed.

Examples

Medical diagnosis

When a doctor attempts to diagnose a patient’s illness, they may make a mistake and misdiagnose the condition. This can have serious consequences for the patient’s health and wellbeing. To reduce error rate, doctors may use diagnostic tests to confirm an initial diagnosis or consult with other doctors to obtain a second opinion.

Credit scoring

Credit scoring assigns a numerical value to an individual’s creditworthiness based on credit history and other financial information. Errors can occur, for example by mistakenly rejecting a loan application from someone with good credit or approving a loan for someone with poor credit. To reduce these errors, credit scoring systems may use more sophisticated algorithms or incorporate additional data sources, such as utility payment history.

Use cases

• Healthcare
• Finance
• Engineering

Notes or pitfalls

• Errors in high-stakes domains (for example, medical diagnosis) can have serious consequences for individuals.
• Reducing error rate often requires additional tools or procedures, such as confirmatory diagnostic tests, consultation with others, more sophisticated algorithms, or inclusion of extra data sources (for example, utility payment history).

Related terms

• Accuracy
• Diagnostic tests
• Credit scoring