When it comes to descriptive statistics, the mean is often the go-to metric. But is it always the best choice? In most cases, the answer is no. The median is a more robust and representative measure of a distribution, and it’s time we give it the recognition it deserves.
## The Mean’s Shortcomings
The mean is sensitive to outliers and skewed distributions, which can lead to misleading conclusions. For instance, if you’re analyzing the salaries of a company, the mean might be skewed by a few extremely high or low values, giving a distorted view of the typical salary.
## The Median’s Advantages
The median, on the other hand, is a more resistant measure that provides a better representation of the typical value in a distribution. It’s less affected by outliers and skewed distributions, making it a more reliable choice in most cases.
## When to Use the Mean
There are some scenarios where the mean is the better choice. For example, if you’re running a casino and want to know how much you expect to earn from each gambler, the mean is a more suitable metric. This is because the law of large numbers comes into play, and the mean converges to the expected value in the long run.
## The Median’s Versatility
But in most cases, the median is a safer bet. It provides a more accurate representation of the typical value in a distribution, and it’s less prone to being skewed by outliers. Whether you’re dealing with symmetric or skewed distributions, the median is a more reliable choice.
## Conclusion
So, should the mean be used almost never in descriptive statistics? Not quite, but it should certainly be used with caution. The median is a more robust and representative measure that deserves more recognition. By defaulting to the median, you can avoid the pitfalls of the mean and get a more accurate picture of your data.
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*Further reading: [Understanding Mean and Median](https://www.statisticshowto.com/mean-median-mode/)*