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What type of new knowledge/added value do we generate when applying statistics?

What type of new knowledge/added value do we generate when applying statistics?. Hans von Storch. We want to learn how the “world” functions, what its state is, what the change and what the perspectives are. For that we apply statistical analysis, or: adopt the statistical concepts Why?

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What type of new knowledge/added value do we generate when applying statistics?

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  1. What type of new knowledge/added value do we generate when applying statistics? Hans von Storch

  2. We want to learn how the “world” functions, what its state is, what the change and what the perspectives are. For that we apply statistical analysis, or: adopt the statistical concepts Why? - because stochasticity is a good “model” to describe a (n otherwise indescribable) deterministic world with “infinitely” many degrees of freedom, which interact nonlinearly (chaotically). Randomness is a suitable model for reality – not a “real property” of the world.

  3. Typical applications in our field: • - Fishing expeditions – checking if “interesting” relationships exist in a data field; regressions, change points, trends. • Confirming / fitting “physical” models/hypotheses in light of empirical evidence (“data”) – the hockey-stick session • These efforts produce new insight into the state, change and functioning of the real world. • Statistical analysis on our field without reference to physical understanding / conceptualizing is sterile.

  4. For me, the best implementation of combined statistical-physical thought is the formulation (and application) of a state-spaced system, which may be integrated forward as a (whatever) Kalman-filter, namely an observation equation Dt = ObsM(Ft) + erroro Combined with a state space equation Ft+1 = DynM(Ft) + errorm This system makes transparent the role of the different types of knowledge (empirical; dynamical), which we blend to produce new knowledge. We saw this approach in Frank Kwasniok’s and Francis Zwiers’ talks (maybe more). More of that, please.

  5. Limitation of statistical analysis in our field: • Significant assumptions are made, the validity of which is not really checked: stationarity, ergodicity • The basic assumption that process of formulation a null-hypothesis is independent from the data used to test the null-hypothesis, is in most climate applications not given, simply, because the collection of sufficiently long series needs too much time. The Mexican Hat-fallacy. • Thus, we adopt a convenient mathematical system, but sometimes forget about this significant and non-trivial assumptions and draw too general conclusions.

  6. Social dimension: The usage of statistical methods in out field has a social dimension, namely - the practice of using new and purportedly advanced methods to project intellectual dominance. The “silver-bullet claims” phenomenon. - the refusal to share data and details of procedure so that others (even hostile challengers) can independently verify claims. Data and details MUST become publicly available and open also for hostile challengers.

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