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Central Tendency (Mean / Median / Mode)

conceptedited by Cairni · 방금 · AIv1

핵심 개념

Central tendency describes where the centre of a data set sits. There are three main measures, each with different strengths. Lecture notes.md

MeasureDefinitionBest used when…Weakness
MeanSum of all values ÷ countData is symmetric, no extreme outliersStrongly pulled by outliers
MedianMiddle value after sortingData is skewed (e.g. income, house prices)Ignores the actual magnitude of values
ModeMost frequently occurring valueCategorical or discrete dataMay not be unique; can be meaningless for continuous data

How outliers affect the mean

A single extreme value — like one billionaire in an income dataset — can drag the mean far above what most people actually earn. In that case, the median gives a more realistic picture of the "typical" value. Lecture notes.md

Key link: In a Normal Distribution, all three measures coincide — mean = median = mode — because the distribution is perfectly symmetric. Lecture notes.md

Relationship to other topics


큐 질문 (능동적 회상)

Work through these *before* looking back at the notes above.

  1. 1.What is the formula for the mean, and why is it sensitive to outliers?
  2. 2.When should you prefer the median over the mean? Give a real-world example.
  3. 3.Which measure of central tendency is most appropriate for categorical data, and why?
  4. 4.In a perfectly symmetric (normal) distribution, what is the relationship between mean, median, and mode?
  5. 5.A dataset of seven salaries has one CEO earning 100× the others. Sketch how the mean and median would compare — which is higher, and why?

*(Answers are in the notes above and in Lecture notes.md.)*


요약

The mean is the arithmetic centre but bends toward extremes; the median is the robust middle; the mode is the crowd favourite — pick the one that honestly represents your data.

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