Compare and contrast analyzing data and drawing conclusions?

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Wiki User · Answer

Analysis or analyzing is the PROCESS of reviewing and studying the data, looking for patterns, trends and commonalities, as well as identifying outliers or anomalies and generally choosing proven statistical (e.g., percentiles, averages) and scientific methods to ascertain some global and often, detailed understanding of the data. Different statistical tools may be considered and selected to analyze the data. DRAWING CONCLUSIONS is applying thought processes to explain what the data represents and what the analysis shows about the data. For example, collecting samples of computer response times during a 24-hour day and identifying periods of fast and slow response times is analysis, and drawing conclusions is perhaps recognizing that the response times are faster between midnight and 6 a.m. (when most people are not working). Analysis requires knowledge of what tools to use to understand the data and how to best compile it for study, while drawing conclusions requires different thought processes that usually involves applying an external knowledge or other general knowledge to explain why the data is what it is, and more importantly, what to do to modify future results (if that is the reason the data is being collected). In the example cited, if the goal is faster response times, either people will be asked to work more from midnight to 6 a.m. or faster processors are required from 6 a.m. to midnight to accomodate the higher demand. From ANALYZing the data, one would CONCLUDE that work needs to be done during the night or more (or faster) processing equipment is needed. Other innovative solutions may also exist and come to fruition during the conclusion drawing phase. Usually, the more complete the understanding (analysis) of the data, the better the conclusion is likely to be.

Wiki User

∙ 15y ago

This answer is:

Wiki User · Answer

Analysis or analyzing is the PROCESS of reviewing and studying the data, looking for patterns, trends and commonalities, as well as identifying outliers or anomalies and generally choosing proven statistical (e.g., percentiles, averages) and scientific methods to ascertain some global and often, detailed understanding of the data. Different statistical tools may be considered and selected to analyze the data. DRAWING CONCLUSIONS is applying thought processes to explain what the data represents and what the analysis shows about the data. For example, collecting samples of computer response times during a 24-hour day and identifying periods of fast and slow response times is analysis, and drawing conclusions is perhaps recognizing that the response times are faster between midnight and 6 a.m. (when most people are not working). Analysis requires knowledge of what tools to use to understand the data and how to best compile it for study, while drawing conclusions requires different thought processes that usually involves applying an external knowledge or other general knowledge to explain why the data is what it is, and more importantly, what to do to modify future results (if that is the reason the data is being collected). In the example cited, if the goal is faster response times, either people will be asked to work more from midnight to 6 a.m. or faster processors are required from 6 a.m. to midnight to accomodate the higher demand. From ANALYZing the data, one would CONCLUDE that work needs to be done during the night or more (or faster) processing equipment is needed. Other innovative solutions may also exist and come to fruition during the conclusion drawing phase. Usually, the more complete the understanding (analysis) of the data, the better the conclusion is likely to be.