The Importance Of Interpretation In Research Statistics: An Overview

Abstract

This paper was originally conceived of as a real research project, as a way of putting theoretical statistical knowledge to work in the area of real life. The author has access to a suitable population for study (institutionalized retardates), but it was soon discovered that the problems incurred in gathering the needed information were too great to be pursued on an undergraduate level. Medical and social records were a- vailable, but they were filled with ambiguities or generally lacking in needed information. For example, the socio-economic factors involved in retardation were of prime consideration, but the needed information could probably have been acquired only through interviews with parents and they may well have been hesitant to divulge such information. Such problems would not be nearly as considerable if Medical records contained complete, standardized information. At the present time, they do not. The intent to deal with the real world will be pursued, then, by looking beyond the statistics themselves to an analysis of what can be said after the data is compiled. A discussion of the tools of statistics is included as a way of presenting the meaning, interpretation and limitation of each tool. The framework will follow a sort of handbook fashion. The author assumes the readers will have taken an elementary statistics course and the intent is to expand this basic knowledge into an awareness of how it is to be interpreted. The importance of such an awareness is twofold; it provides guidelines for conducting the research and it also determines what can be said once the research itself is completed. The presentation will move in the same sequence as an actual research project would proceed: 1) Method of Collecting Data (Chapter i) 2) Grouping and Compiling Data (Chapter 2) 3) Describing the Distribution Mathematically (Chapter 3) 4) Statistical Inference: Interpretation of Results (Chapter 5) Non-Parametric Methods (Chapter 5) Whenever possible, real examples are included in order to illustrate a given subject. Explanations are short and to the point so as to present * an overview rather than a detailed analysis. Since rigorous computation formulas are readily available, the intent here is to explore the logic of the methods to be usedThis paper was originally conceived of as a real research project, as a way of putting theoretical statistical knowledge to work in the area of real life. The author has access to a suitable population for study (institutionalized retardates), but it was soon discovered that the problems incurred in gathering the needed information were too great to be pursued on an undergraduate level. Medical and social records were a- vailable, but they were filled with ambiguities or generally lacking in needed information. For example, the socio-economic factors involved in retardation were of prime consideration, but the needed information could probably have been acquired only through interviews with parents and they may well have been hesitant to divulge such information. Such problems would not be nearly as considerable if Medical records contained complete, standardized information. At the present time, they do not. The intent to deal with the real world will be pursued, then, by looking beyond the statistics themselves to an analysis of what can be said after the data is compiled. A discussion of the tools of statistics is included as a way of presenting the meaning, interpretation and limitation of each tool. The framework will follow a sort of handbook fashion. The author assumes the readers will have taken an elementary statistics course and the intent is to expand this basic knowledge into an awareness of how it is to be interpreted. The importance of such an awareness is twofold; it provides guidelines for conducting the research and it also determines what can be said once the research itself is completed. The presentation will move in the same sequence as an actual research project would proceed: 1) Method of Collecting Data (Chapter i) 2) Grouping and Compiling Data (Chapter 2) 3) Describing the Distribution Mathematically (Chapter 3) 4) Statistical Inference: Interpretation of Results (Chapter 5) Non-Parametric Methods (Chapter 5) Whenever possible, real examples are included in order to illustrate a given subject. Explanations are short and to the point so as to present * an overview rather than a detailed analysis. Since rigorous computation formulas are readily available, the intent here is to explore the logic of the methods to be used