Mean, Median, Mode (Central Tendency)

Three different ways to measure the 'center' of a distribution, each revealing different information. Mean (average) is sensitive to outliers—one billionaire drastically raises the 'average' income. Median (middle value when sorted) is robust to outliers—it captures what the typical person experiences. Mode (most common value) identifies the peak—what's most likely. In symmetric distributions (bell curves), all three converge. In skewed distributions (income, company sizes, website traffic), they diverge dramatically—and using the wrong one produces misleading conclusions. Choosing the right measure of center IS the analysis in many real-world situations.

When to use it

When evaluating 'average' claims that might be misleading; when communicating data to stakeholders and need to choose the most honest summary; when the distribution of a variable is highly skewed; when detecting whether statistical summaries are being used to mislead.

How it can help

Before accepting or presenting any 'average,' ask: mean, median, or mode? In right-skewed distributions (income, company revenue, social media followers), the mean is pulled up by outliers and doesn't represent typical experience—use the median. For frequency-based questions (what's the most common outcome?), use the mode. For aggregate totals where every value contributes (total revenue, total weight), the mean is appropriate. The most common error: presenting or accepting a mean as if it represents a typical case in a skewed distribution.