What is the difference between mean, median, and mode?

Mean is the arithmetic average (sum of all values divided by count). Median is the middle value when data is ordered (or average of two middle values). Mode is the most frequently occurring value. Each describes 'center' differently: mean uses all values, median resists outliers, and mode shows the most common value.

When should I use median instead of mean?

Use median when your data has outliers or is skewed. For example, in income data where a few very high earners could distort the mean, median gives a better sense of the 'typical' value. Median is also preferred for ordinal data (rankings, ratings) where arithmetic averaging doesn't make sense.

Can a dataset have multiple modes?

Yes. A dataset with one mode is unimodal, two modes is bimodal, and three or more is multimodal. If all values appear with equal frequency, there's no mode. Multiple modes can reveal interesting patterns, such as two distinct groups within your data (e.g., two peak buying times).

How do I calculate median with an even number of values?

With an even number of values, there's no single middle value. First, sort the data in order. Then take the two middle values and calculate their average. For example, with the dataset [3, 7, 9, 15], the median is (7 + 9) / 2 = 8.

Why would mean, median, and mode be different?

These measures differ when data is skewed or has outliers. In a perfectly symmetrical distribution (like a bell curve), they're equal. In right-skewed data (few high values), mean > median > mode. In left-skewed data (few low values), mode > median > mean. The difference between them reveals the shape of your data distribution.