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The mean is one of the most common measures of central tendency in statistics. It is often referred to as the average. Calculating the mean involves adding up all the numbers in a dataset and then dividing by the count of those numbers. This gives an overall idea of where the central point of a data set lies, on average.
For example, with the dataset \[1, 2, 2, 3, 9\], you calculate the mean by adding all these numbers together to get 17. Then, you divide by the total number of data points, which is 5, resulting in a mean of 3.4.

• The mean is useful in datasets where the values are evenly spread out.
• It is sensitive to outliers, meaning if extreme values exist, they can skew the mean significantly.

If you want a quick snapshot of overall performance or central value across all data points, the mean can be a good choice. However, beware of its limitations in the presence of outliers.

The median is another important measure of central tendency and offers a different insight than the mean. To find the median, you must arrange all numbers in a dataset in ascending order and identify the middle number. If there is an even count of numbers, the median is the average of the two middle numbers.
Consider the same dataset again, \[1, 2, 2, 3, 9\]. Here, arranging the numbers already looks like this. The median, being the middle number in this ordered list, is 2.

• The median is robust because it is not affected by outliers or extremely skewed data.
• It provides a better central tendency measure if a dataset includes anomalies or skewed distributions.

When evaluating health data or income levels, where extreme values can exist, the median often provides a more accurate representation of "typical" values.

A skewed distribution is when a dataset does not symmetrically distribute around its mean. This can occur when extreme values pull the mean in their direction, making the median a potentially more accurate representation of central tendency.
For example, in our dataset \[1, 2, 2, 3, 9\], the number 9 is much higher than the other values, causing a right-skew or positive skew. This skew pulls the mean up to 3.4, which might not accurately represent the central point of most data in the set, especially if you're looking for typical value characteristics.

• In positively skewed data, the mean is higher than the median.
• Conversely, in negatively skewed data, extreme low values pull the mean down, making it less than the median.

Understanding the shape of your data’s distribution helps in deciding which measure of central tendency—mean or median—is most appropriate for accurately representing your data.