Pros and Cons
Pros
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Unaffected by Outliers: Unlike mean, the mode is not affected by extreme values or outliers. This makes it a stable measure in datasets with extreme values.
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Applicable to All Data Types: While mean and median require numerical data, the mode can be applied to nominal data (like categories) as well, making it versatile.
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Intuitive and Easy to Understand: The concept of "most frequent occurrence" is straightforward and easy for most people to grasp.
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Multiple Modes: A dataset can have more than one mode, which can be useful in understanding the distribution of the data.
Cons
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Not Always Available: Some datasets might not have any mode, especially if no value repeats. In contrast, mean and median always exist for a set of data.
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Multiple Modes Can Be Ambiguous: While it can be beneficial to identify multiple modes in a dataset, as it offers a broader perspective, it can also be a drawback because it doesn't pinpoint a singular, definitive central value.
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Sensitive to Sample Size: Small changes in the dataset can result in a different mode or cause the mode to disappear, making it less reliable for small datasets.
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May Not Represent Central Tendency: In certain distributions, the mode might not fall near the center of the data, making it less representative of the dataset as a whole.
Summary
The mode, as a measure of central tendency, has its unique advantages, such as being unaffected by outliers and being applicable to all data types. However, it also has limitations like not always being available and potentially not representing the central tendency well. Choosing the mode or any other measure (mean, median) should be based on the nature of the data and the specific insights one is looking to derive.