Using Statistics in Decision Making

Objectives or Hypotheses: What are the objectives of the study or the questions to be answered? What is the population to which the investigators intend to refer their findings?

Statistical Design: Is the study a planned experiment (i.e., primary data), or an analysis of records ( i.e., secondary data)? How is the sample to be selected? Are there possible sources of selection, which would make the sample atypical or non-representative? If so, what provision is to be made to deal with this bias? What is the nature of the control group, standard of comparison, or cost? Remember that statistical modeling means reflections before actions.

Observations: Are there clear definition of variables, including classifications, measurements (and/or counting), and the outcomes? Is the method of classification or of measurement consistent for all the subjects and relevant to Item No. 1.? Are there possible biased in measurement (and/or counting) and, if so, what provisions must be made to deal with them? Are the observations reliable and replicable (to defend your finding)?

Analysis: Are the data sufficient and worthy of statistical analysis? If so, are the necessary conditions of the methods of statistical analysis appropriate to the source and nature of the data? The analysis must be correctly performed and interpreted.

Conclusions: Which conclusions are justifiable by the findings? Which are not? Are the conclusions relevant to the questions posed in Item No. 1?

Representation of Findings: The finding must be represented clearly, objectively, in sufficient but non-technical terms and detail to enable the decision-maker (e.g., a manager) to understand and judge them for himself? Is the finding internally consistent; i.e., do the numbers added up properly? Can the different representation be reconciled?

Managerial Summary: When your findings and recommendation(s) are not clearly put, or framed in an appropriate manner understandable by the decision maker, then the decision maker does not feel convinced of the findings and therefore will not implement any of the recommendations. You have wasted the time, money, etc. for nothing.


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