Trimming the mean can be a valuable measurement tool when used correctly . It eliminates the extreme values within a set (the highest and lowest ten percent ) in to deal with the data that is most closely related and relevant to the study . In the study of obesity trimming the mean is not a reliable method of obtaining information When studying obesity , one is less concerned with the norm and more concerned with those measurements that are out of the norm . This is because the study of obesity needs to include the best and [banner_entry_middle]
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There are advantages to trimming the mean in this situation . The first depends on what the study hopes to achieve . They may need men who have similar heights and weights in to observe behavior over a period of time . In this case , the highest and lowest heights and weights would throw off the average . In addition , the men with similar body types might have similar reactions to the study while the men on the fringes may provide data that is not relevant
The first disadvantage to trimming the mean is that this is a relatively small study only ten men are included . Eliminating ten percent of the members on each end would bring the study down to eight men , and this population might not be large enough to obtain accurate results that can be duplicated . In a study this small , researchers should prefer to have a range of heights and weights , because they can be easily tracked . Finally , one important disadvantage to trimming the mean is that people are not numbers each person is unique and will present with different circumstances that may have led to obesity . If the most extreme cases are weeded out , then that data will not be included in the study and it may be relevant to the origins of obesity If the study included several hundreds or thousands of people , then it would be necessary to trim the mean in to bring the study down to a size that is easily manageable… [banner_entry_footer]
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