Best practices is statistical analysis are stringent guidelines that tailored to reduce the degree of discrepancy in the results and prevent derivation of erroneous data (Few, 2009). Ideally, skewed conclusions are products of errors in the data. Therefore, if the errors are prevalent, the conclusions drawn from the information are bound to be accurate.
Best practices entail selecting the best interval and keeping intervals consistent (Few, 2009). Keeping intervals consistent is a rule that dictates the choice of a categorical scale when comparing data; the scale must be equal. For instance, when presenting data on a histogram, choosing a scale that is not consistent will result in the wrong presentation of results. Therefore, the right correlations between the data sets cannot be established. However, there is an exception to the equal intervals rule. Holistically, unequal intervals can be used when a majority of the values fall within a particular range. Moreover, selecting the best interval is another best practice in the presentation of statistical data. The number of intervals to which the value range of values is divided into should be able to allow accurate depiction of findings. For instance, too many intervals result in a ragged distribution, and too few intervals form incomprehensive results since more data is clumped into one interval. The generalization of the data as a result of the use of few intervals contribute to loss of meaning.
Delegate your assignment to our experts and they will do the rest.
Principally, both best practices are integral in ensuring that data is presented accurately through the usage of a consistent interval. Consistency in the interval allow easy comparison of the data groups. Also, the choice of the best interval prevents ragged distribution and utter generalization of data. In this regard, observing the best practices allows an analyst to present data without distorting the meaning. Therefore, I agree with the assumption that best practices are imperative in data presentation. I will use the best practices presented in the text in my future role as a researcher and a data analyst to present information in a comprehensible manner.
References
Few, S. (2009). Now you see it: simple visualization techniques for quantitative analysis . Analytics Press.
