Mistakes knowledge based

In Figure 2.6, the slips/mistakes distinction is further elaborated by relating it to the Rasmussen SRK classification of performance discussed earlier. Slips can be described as being due to misapplied competence because they are examples of the highly skilled, well practiced activities that are characteristic of the skill-based mode. Mistakes, on the other hand, are largely confined to the rule and knowledge-based domains. 

Reason divided error into slips, or lapses, and mistakes. Slips or lapses involve the correct choice of a method to achieve a task, but a failure to carry it out correctly. Mistakes on the other hand involve a flaw in the plan which means that even if it is carried out correctly, the objective isn t achieved. Slips and lapses are often skill-and rale-based errors, whereas mistakes are often knowledge-based errors. 

Skill- and rale-based decisions are the basis of routine activities. As a result, there are more absolute numbers of errors in such decisions, but the percentage of errors is much lower than in knowledge-based decisions. The types of errors in activities based on skill- and rale-based decisions are more likely to be what are called slips and lapses . Slips come from attentional failure, lapses from memory failure - they are two types of unintended actions. The types of errors in knowledge-based decisions are more likely to be what are distinguished as mistakes - errors in planning a path to an outcome. Mistakes are a type of intended action, and may be rule-based or knowledge-based. They occur when the selection of an objective and the means to achieve it are faulty, whether or not the actions go as planned. 

Human errors fall into three groups - slips, lapses and mistakes, which can be further sub-divided into rule-based and knowledge-based mistakes. 

Mistakes occur when an incorrect action takes place but the person involved believes the action to be correct. A mistake involves an incorrect judgement. There are two types of mistake - rule-based and knowledge-based. 

Knowledge-based mistakes occur when well-tried methods or calculation rules are used inappropriately. For example, the depth of the foundations required for a particular building was calculated using a formula. The formula, which assumed a clay soil, was used to calculate the foundation depth in a sandy soil. The resultant building was unsafe. 

Rule and knowledge-based errors are also collectively termed mistakes. They represent intentional actions being incorrectly carried out. Sometimes the error and its evidence are ignored and dismissed due to the operator s belief that they have in fact acted correctly. 

Knowledge-based mistakes Unreliable behavior Problem solving... 

A further relationship exists between these error Qpes and Rasmussen s [3] classic model of skill, rule and knowledge-based behaviour. Slips and l >ses tend to occur at the skill-based level, whereas mistakes occur at the rule-based and knowledge-based levels. Rule-based mistakes are primarily due to mis lied expertise, where some pre-established plan or problem solution is lied inappropriately. Knowl ge-based mistakes generally occur due to a lack of e q)ertise, where no off-the-shelf solution exists and an individual is forced to work out a plan of action fiom first principles. 

Knowledge-based mistakes are more complex and are often due to an incomplete or inaccurate understanding of the situation. 

The outcome of hydrochemical studies is based on chemical data and their interpretation. A clear knowledge of basic terms, units applied, and the nature of analytical errors is essential to avoid confusion and mistakes. The following concepts and definitions have to be mastered as a prerequisite to data processing. 

The blind application of statistical tests to retain or reject a suspect measurement in a small set of data is not likely to be much more fruitful than an arbitrary decision. The application of good judgment based on broad experience with an analytical method is usually a sounder approach. In the end, the only valid reason for rejecting a result from a small set of data is the sure knowledge that a mistake was made in the measurement process. Without this knowledge, a cautious approach to rejection of an outlier is wise. 

Required Knowledge (1) The laboratory procedures for acid-base titrations. (2) The mathematical effect each possible mistake will have on the calculated result. 

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