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The arithmetic mean is the most commonly used measure of the central tendency of a data set. It is defined as the sum of all the elements constituting the data set, divided by the total number of elements. It is sometimes loosely referred to as the “average.”

When all the values in a data set are not unique, the sum in the numerator can be calculated by multiplying each distinct value by its frequency.

Sometimes, the arithmetic mean of a sample can be affected by a few data points that are significantly different from the rest, outliers. For example, if in a sample of ten students, nine students have ages varying 20 and 21 while one student is ten years old, then the arithmetic mean would be less than 20, which is not a true representation of the central tendency of the students’ ages. Hence, careful consideration is required before using the arithmetic mean to measure the central tendency of a data set.

The arithmetic mean of a qualitative data set cannot be calculated. For example, in a class of students with different nationalities, there is no arithmetic mean for nationality.

Tags
Arithmetic MeanCentral TendencyData SetAverageOutliersFrequencyQualitative DataRepresentationCalculation

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3.2 : Arithmetic Mean

Measure of Central Tendency

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3.1 : What is Central Tendency?

Measure of Central Tendency

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3.3 : Geometric Mean

Measure of Central Tendency

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3.4 : Harmonic Mean

Measure of Central Tendency

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3.5 : Trimmed Mean

Measure of Central Tendency

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3.6 : Weighted Mean

Measure of Central Tendency

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3.7 : Root Mean Square

Measure of Central Tendency

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3.8 : Mean From a Frequency Distribution

Measure of Central Tendency

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3.9 : What is a Mode?

Measure of Central Tendency

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3.10 : Median

Measure of Central Tendency

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3.11 : Midrange

Measure of Central Tendency

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3.12 : Skewness

Measure of Central Tendency

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3.13 : Types of Skewness

Measure of Central Tendency

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