1) Arithmetic mean rigidly defined by Algebraic Formula.

2) It is easy to calculate and simple to understand.

3) It is based on all observations of the given data.

4) It is capable of being treated mathematically hence it is widely used in statistical analysis.

5) Arithmetic mean can be computed even if the derailed distribution is not known but some of the observation and number of the observation are known.

6) It is least affected by the fluctuation of sampling.

7) For every kind of data mean can be calculated.

1) It can neither be determined by inspection or by graphical location.

2) Arithmetic mean can not be computed for qualitative data like data on intelligence honesty and smoking habit etc.

3) It is too much affected by extreme observations and hence it is not adequately represent data consisting of some extreme point.

4) Arithmetic mean can not be computed when class intervals have open ends.

5) If any one of the data is missing then mean can not be calculated.

1) Mean is used in fields such as business, engineering and computer science.

2) It is used in report card or in our population.

3)In addition to mathematics and statistics, the arithmetic mean is used frequently in fields such as economics, sociology, and history, though it is used in almost every academic field to some extent. For example, per capita GDP gives an approximation of the arithmetic average income of a nation's population.

4)While the arithmetic mean is often used to report central tendencies, it is not a robust statistic, meaning that it is greatly influenced by outliers.

5) It’s used to compute the variance and SD (Standard Deviation). It’s good for inferential statistics.

Measures of central tendency

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