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14 Statisticians who use a computer for data analysis invariably take one

15 of two approaches (considered in the extremes here for illustration):

18 information into the computer program code used for the data

19 analysis, or

21 similar approaches, where tasks are facilitated, sometimes with

22 accompanying workflow support.

24 Both approaches have co-existed since the early 80s, with the FORTRAN

25 approach dating back to the dawn of the computing era.

30 Statistics consists of a range of procedures that can be applied to

31 make decisions. However, the range of procedures and resulting

32 interpretation makes it difficult to actually drive a cook-book style

33 approach. But it remains that there is a color-palette of procedures

34 which can be used to support decision making from collected

35 datatsets.

37 define a statistical procedure as a decision-making approach which

38 entails the intertwining of formal and informal structure.

40 Components, first pass

44 inherently different, others just look different): example, T-test

45 is a simplified ANOVA, so look different. Or ML-based (with a

46 hill-climb) vs. ML with a LS fit (look different) vs. bayesian

47 Linear regression (inherently different).

49 assessment/comparison with other reference behaviours or

50 probabilistic processes.

52 that conclusion (when data is present)

56 represented by a real class, which can then be instantiated through

57 the application of data.

59 Components from Gelman:

62 \item measurement

63 \item comparison

64 \item ??normalization??

66 to characterize the application of a statistical procedure.

71 By example, consider the t-test as an instance of a procedure,

72 representing the general class of testing hypotheses surrounding 2

73 means. Related would be formal likelihood tests with distributions,

74 the superspace/classes from regression and ANOVA.

75 Questions could be:

78 \item what is the difference?

79 \item what is the strength of the difference?

82 One component (from Gelman's dicotomy) -- comparison. And decision

83 could equal ( comparsion, extremeness from expectation ).

89 This is the construction of the model and parameters that would be

90 used to form the term used to make the assessment. Here, we could

91 consider

96 as the fundamental quantity to compare. This can arise from many

97 sources such as regression models

103 or

111 Let $X=(Y,G)$ from above, the whole data.

113 empirical adjustment:

119 or regression-model-based:

125 or likelihood-model-based: (FIXME!)

131 or score-model-based:

140 Value or Range on the Target Scale (existing parameter describing

141 data-oriented substantive model)

143 Translation of Value/Range on the Decision Scale (what to do, what to

144 decide about the problem, i.e. in a testing framework).