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Advantages and Disadvantages of Frequency Distributions

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A frequency distribution divides observations into the data set into conveniently established, numerically ordered classes (groups or categories). The number of observations in each class is referred to as frequency.

A few examples of instances where frequency distributions would be useful are:

  1. A marketing manager wants to know how many units (and what proportions or percentage) of each product sells in a particular region during a given period.
  2. A professor wants to organize the marks obtained by a class of 500 students in a freshman calculus course. The data can be easily expressed in the form of a frequency distribution table.

We now list out some of the advantages and disadvantages of frequency distributions and frequency distribution tables.

Advantages of Frequency Distribution:

  1. The data are expressed in a more compact form. One can get a deeper insight into the salient characteristics of the data at the very first glance.
  2. One can quickly note the pattern of distribution of observations falling into various classes.
  3. It permits the use of more complex statistical techniques which help reveal certain other obscure and hidden characteristics of the data.
  4. The frequency distribution table can be used to construct histograms, bar graphs, and other diagrams which can help us in visualizing the data.
  5. We can use the frequency table to calculate various descriptive measures of the data such as the mean, median, mode, standard deviation, skewness, and kurtosis.

Disadvantages of Frequency Distribution:

  1. In the process of grouping, individual observations lose their identity. It becomes difficult to notice how the observations contained in each class are distributed. This applies more to a frequency distribution that uses the tally method in its construction.
  2. A serious limitation inherent in this kind of grouping is that there will be too much clustering of observations in various classes in case the number of classes is too small. This will cause some of the essential information to remain unexposed. Hence, it is important that summarizing data should not be at the cost of losing essential details. The purpose should be to seek an appropriate compromise between having too many details or too few.

References:

Fundamentals of Business Statistics – JK Sharma

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