

Table: Definitions  Certainty, risk, and uncertainty Introduction: These definitions are consistent with those commonly used, e.g. Taha [1987, page 428]. 

Certainty 
Risk 
Uncertainty 

Implies perfect information. All relevant
information to the problem is known. 
Implies partial information. Some of all
the relevant information to the problem is stochastic. 
Implies incomplete information. Some of
all the relevant information to the problem is missing. 

Variables (x) are known with certainty: _{} if quantitative. _{}if qualitative it may either be present with certainty or not be present. 
Variables may include certainty variables but at least one variable
is random and represented by a probability density function (PDF): _{} if quantitative. _{} if qualitative it may either occur, with probability_{} or it may not occur, with probability (1p). 
Variables may include risk variables but now at least one is either unknown or cannot be determined: X_{u} is unknown. Something is influencing the problem but we do not know what it is. In statistical tests this compares to the influence of the residuals. X_{i} is known but its value is indeterminate. It can’t be reasonably approximated with a PDF. 

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