Mean vs. Median vs. Mode: When to Use Which
Use the mean when your data doesn't have extreme outliers and you want a value that accounts for every number's exact magnitude; use the median when outliers or a skewed distribution could distort the mean, since the median only cares about relative order, not exact size; use the mode when you specifically want the single most common value, which matters most for categorical or repeated-value data rather than continuous measurements.
These three measures of central tendency can tell very different stories about the same dataset, which is why choosing the right one for the situation matters.
Why they can diverge so much
For a symmetric dataset with no outliers, mean and median land close together. But a dataset with a few extreme values — a handful of very high salaries in an otherwise modest-income group, for example — pulls the mean noticeably higher than the median, which stays anchored to the middle regardless of how extreme the outliers are.
A practical example
Median home price is reported far more often than mean home price specifically because a small number of ultra-expensive homes would otherwise pull the mean well above what a "typical" home in the area actually costs — the median gives a more representative sense of the middle of the market.