Arithmetic average, or *arithmetic mean*, or just *mean*, is probably the simplest tool in statistics, designed to measure central tendency in a data set (which can be a group of stocks or returns of a stock in particular years). Using arithmetic average has advantages and disadvantages, and in some cases you may find other measures (like geometric average or median) more suitable.

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## Advantage 1: Fast and easy to calculate

As the most basic measure in statistics, **arithmetic average is very easy to calculate**. For a small data set, you can calculate the arithmetic mean quickly in your head or on a piece of paper. In **computer programs** like Excel, the arithmetic average is always one of the most basic and best known functions (in Excel the function is AVERAGE). Here you can see the basics of arithmetic average calculation.

## Advantage 2: Easy to work with and use in further analysis

Because its calculation is straightforward and its meaning known to everybody, **arithmetic average** is also more comfortable to **use as input to further analyses and calculations**. When you work in a team of more people, the others will much more likely be familiar with *arithmetic average* than *geometric average* or *mode*.

## Disadvantage 1: Sensitive to extreme values

**Arithmetic average is extremely sensitive to extreme values**. Imagine a data set of 4, 5, 6, 7, and 8,578. The sum of the five numbers is 8,600 and the mean is 1,720 – which doesn’t tell us anything useful about the level of the individual numbers.

Therefore, **arithmetic average** is not the best measure to use with data sets containing a few **extreme values** or with more **dispersed (volatile) data sets** in general. *Median* can be a better alternative in such cases.

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## Disadvantage 2: Not suitable for time series type of data

**Arithmetic average** is perfect for measuring central tendency when you’re working with data sets of independent values taken at one point of time. There was an example of this in one of the previous articles, when we were calculating average return of 10 stocks in one year.

However, in finance you often work with percentage returns over a series of multiple time periods. For **calculating average percentage return over multiple periods of time**, **arithmetic average is useless**, as it fails to take the different basis in every year into consideration (100% equals a different price or portfolio value at the beginning of each year). The more volatile the returns are, the more significant this weakness of arithmetic average is. Here you can see the example and reason why arithmetic average fails when measuring average percentage returns over time.

## Disadvantage 3: Works only when all values are equally important

Arithmetic average treats all the individual observations equally. In finance and investing, you often need to work with unequal weights. For example, you have a portfolio of stocks and it is highly unlikely that all stocks will have the same weight and therefore the same impact on the total performance of the portfolio.

Calculating the average performance of the total portfolio or a basket of stocks is a typical case when **arithmetic average is not suitable** and it is better to use weighted average instead. You can find more details and an example here: Why you need weighted average for calculating total portfolio return.

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## Conclusion

**Arithmetic average** as a measure of central tendency is simple and easy to use. But in order to take advantage of it and prevent it from doing any harm to your analysis and decision making, you should be familiar with the **situations when it fails** and when other tools are more useful.

You can easily calculate arithmetic average, median and other measures using the Descriptive Statistics Excel Calculator.