Mean, median, and mode each locate the "center" of a dataset differently.
Mean (sum ÷ count) is sensitive to outliers; median (middle value) is resistant to them.
Mode is the most frequent value — best for categorical data; a dataset can be bimodal or have no mode.
Which measure to use depends on the shape of the data and whether outliers are present.
Mean vs. Median vs. Mode
Measure
How it's calculated / best use
Mean
Sum of all values ÷ count — high outlier sensitivity; best for normal data without outliers
Median
Middle value of sorted data (avg of two middle values if even count) — low outlier sensitivity; best for skewed data
Mode
Most frequent value — not applicable to outliers; best for categorical data
Key Terms
Mean
Sum of all values divided by the number of values (the average).
Median
The middle value of a dataset sorted in ascending order.
Mode
The value that appears most frequently in a dataset.
Bimodal
A dataset where two values tie for the highest frequency.
Common MCAT Trap
An outlier pulls the mean toward it but barely moves the median — if a question adds an extreme value, expect the mean (not the median) to shift dramatically.
A dataset can have zero modes (no repeated value) or more than one mode (bimodal) — don't assume every dataset has exactly one.
Quick Recall
Dataset {7, 3, 4, 1, 7, 6} — replacing the 6 with 100 changes which measure most?
Which measure of central tendency is best for categorical survey data?
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