Welcome to NUSAP net

Robust knowledge for Sustainability

Post Normal Science
Participatory IA
Sensitivity Analysis
Expert elicitation
Uncertainty Communication
Pluralistic uncert. management

Reports, papers, ...
RIVM/MNP Uncertainty Guidance
Model quality checklist
Interactive tools
Pedigree matrices

Related websites
Post Normal Times
The Integrated Assessment Society
Precautionary Principle
Renewable Energy Master

Main Menu
Web Links
Your Account
Top 10

Other Options
· Members List

Welcome to NUSAP net Glossary

[ A |  B |  C |  D |  E |  F |  G |  H |  I |  J |  K |  L |  M |  N |  O |  P |  Q |  R |  S ]
[ T |  U |  V |  W |  X |  Y |  Z |  1 |  2 |  3 |  4 |  5 |  6 |  7 |  8 |  9 |  0 ]

[ 1 ]
Terms Definitions
Validation Validation is the process of comparing model output with observations of the 'real world'. Validation can not 'validate' a model as true or correct, but can help establish confidence in a model's utility in cases where the samples of model output and real world samples are at least not inconsistent. For a fuller discussion of issues in validation, see Oreskes et al., (1994).
Value diversity
Value-ladenness Value-ladenness refers to the notion that value orientations and biases of an analyst, an institute, a discipline or a culture can co-shape the way scientific questions are framed, data are selected, interpreted, and rejected, methodologies are devised, explanations are formulated and conclusions are formulated. Since theories are always underdetermined by observation, the analysts' biases will fill the epistemic gap which makes any assessment to a certain degree value-laden.
Variability In one meaning of the word, variability refers to the observable variations (e.g. noise) in a quantity that result from randomness in nature (as in 'natural variability of climate') and society.
In a slightly different meaning, variability refers to heterogeneity across space, time or members of a population. Variability can be expressed in terms of the extent to which the scores in a distribution of a quantity differ from each other. Statistical measures for variability include the range, mean deviation from the mean, variance, and standard deviation.
[ 1 ]