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| Titel |
From inferential statistics to climate knowledge |
| VerfasserIn |
A. H. N. Maia, H. Meinke |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7340
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| Digitales Dokument |
URL |
| Erschienen |
In: 1st Alexander von Humboldt International Conference ; Nr. 6 (2006-02-14), S.211-216 |
| Datensatznummer |
250003261
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| Publikation (Nr.) |
 copernicus.org/adgeo-6-211-2006.pdf |
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| Zusammenfassung |
| Climate variability and change are risk factors for climate sensitive
activities such as agriculture. Managing these risks requires "climate
knowledge", i.e. a sound understanding of causes and consequences of climate
variability and knowledge of potential management options that are suitable
in light of the climatic risks posed. Often such information about
prognostic variables (e.g. yield, rainfall, run-off) is provided in
probabilistic terms (e.g. via cumulative distribution functions, CDF),
whereby the quantitative assessments of these alternative management options
is based on such CDFs. Sound statistical approaches are needed in order to
assess whether difference between such CDFs are intrinsic features of
systems dynamics or chance events (i.e. quantifying evidences against an
appropriate null hypothesis). Statistical procedures that rely on such a
hypothesis testing framework are referred to as "inferential statistics" in
contrast to descriptive statistics (e.g. mean, median, variance of population
samples, skill scores). Here we report on the extension of some of the
existing inferential techniques that provides more relevant and adequate
information for decision making under uncertainty. |
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