Reaction of a certain article - Essay Example

Reaction to the article The Median Isnt the Message The median of a dataset is basically the numerical value that separates the lower half of a population, probability distribution or sample from the higher half (Stuart 45). The median of a finite dataset can be established by arranging all the observations from the least to the highest and picking the one in the middle. If the number of observations is even, the median can be obtained by calculating the mean of the two middle values. In other words, the median is the middle value in the list.

Worth noting is that it is not always the case that no member of the data set has a value that equals the median, if the number of observations are even (Stuart 52). The median is a measure of central tendency in that it is a statistic that generalizes the typical values of members of a sample or population. The median as a measure of central tendency and location parameter is robust and resistant to the effects of outliers, compared to the mean and mode. A data set can be symmetric or skewed.

When a data set is symmetric, the right and left hand sides of the distribution are to a great extent balanced around the mean (Stuart 62). Typically, a symmetric distribution is one in which the mean, mode and median are equal or almost equal. The left and right tails of the distribution, therefore, have more or less the same length. On the other hand, when a data set is skewed, the right and left hand sides of the distribution are not balanced around the mean (Rider 149). The measures of central tendency associated with the distribution also tend to be unequal.

Furthermore, the tails of the distribution tend to be unequal; one longer than the other. Generally, the skewness of a distribution may hint to the presence of outliers or measurement errors. In his article, The Median Isnt the Message, Jay Gould rightly defines median as “the half-way point” (Gould par. 2). Gould also rightly notes that the median is a measure of central tendency which, like the mean, can be exploited to one’s advantage when generalizing situations (Gould par. 2). In his article, Gould seeks to convince his audience that the median is not a perfect measure of a distribution and may be subject to misinterpretation.

The author also seeks to clarify that in as much as the median is a measure of central tendency, its interpretation depends on whether or not the distribution is normal or skewed, and, if skewed, the direction of the skew. In his case for example, he would have assumed that he had just about eight months to live owing to his medical condition, ignoring the skew in the distribution. By considering the direction of the skew, Gould realizes that his chances of surviving for a longer period than the median are high taking into consideration the direction of the skew of the distribution.

In conclusion, the thesis of the article is convincing. In as much as the median generalizes a data set or distribution, the presence of outliers impact on its interpretation and, therefore, its message. Works CitedGould Jay. The Median Isnt the Message. Rider, Paul. Variance of the median of small samples from several special populations. J. Amer. Statist. Assoc. 55 (289): 148–150. 1960. Print. Stuart, Alan & Ord, Keith. Kendalls Advanced Theory of Statistics. London: Arnold. 1994. Print.