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Climate variability and change are risk factors for climate sensitiveactivities such as agriculture. Managing these risks requires "climateknowledge", i.e. a sound understanding of causes and consequences of climatevariability and knowledge of potential management options that are suitablein light of the climatic risks posed. Often such information aboutprognostic variables (e.g. yield, rainfall, run-off) is provided inprobabilistic terms (e.g. via cumulative distribution functions, CDF),whereby the quantitative assessments of these alternative management optionsis based on such CDFs. Sound statistical approaches are needed in order toassess whether difference between such CDFs are intrinsic features ofsystems dynamics or chance events (i.e. quantifying evidences against anappropriate null hypothesis). Statistical procedures that rely on such ahypothesis testing framework are referred to as "inferential statistics" incontrast to descriptive statistics (e.g. mean, median, variance of populationsamples, skill scores). Here we report on the extension of some of theexisting inferential techniques that provides more relevant and adequateinformation for decision making under uncertainty

From inferential statistics to climate knowledge