DB 1: A Strange Distrubution Of Scores – Article Example

The Distribution of Scores s Measures of variability show the extent to which the set of scores is spread out within a set of data. In the scenario provided in this case where the six students had similar scores, using the range alone would not be appropriate to show the spread of the data. The range might ignore much information because it only considers the highest and the lowest scores. In interpreting the data in the scenario, other measures of variability might be important in giving more information that would show the spread and dispersion of the data. Six students in the data received almost the same score thus clustering the scores around the median. During the interpretation of such a data set, it would be ideal and crucial for the instructor to compute the quartiles and the standard deviation to supplement the range. The interquartile range might be helpful in indicating dispersion within the data (Lane, 2014).
It may be important because it spans 50 percent of the data set thus eliminating any outliers. While interpreting the data, the interquartile range may include the similar scores. Thus, helps give more information about the spread of the data. The standard deviation would be more informative for the data set because it summarizes the magnitude by which every value differs from the mean of the whole distribution. Interpretation of the data set would require all these measures of variability to make it more informative. However, the standard deviation would be an ideal measure because unlike the range and the quartiles, it considers every variable in the data (Lane, 2014). In this case, it will consider even the six similar scores in the data set.
Reference
Lane, D. (2014). Measure of Variability. Retrieved from Onlinestatbook.com: http://onlinestatbook.com/2/summarizing_distributions/variability.html