The Median is a measure of central tendency that represents the exact middle value in a sorted list of numbers. Unlike the mean (average), which can be skewed by extremely high or low numbers, the median provides a better representation of the "typical" value in skewed datasets.
[Image of calculating median in data set]Think of the median as the dividing line that separates the higher half of the data from the lower half.
1. How to Find the Median
To find the median, you must follow one crucial rule first: Order the numbers from smallest to largest.
Scenario A: Odd Amount of Numbers
If you have an odd number of data points (e.g., 5, 7, 9 numbers), the median is the single number sitting right in the center.
Example: Find the median of {3, 1, 9, 2, 6}.
- Order them: 1, 2, 3, 6, 9
- Find the middle: The number 3 is exactly in the middle.
- Median = 3.
Scenario B: Even Amount of Numbers
If you have an even number of data points (e.g., 4, 6, 8 numbers), there is no single middle number. Instead, there are two middle numbers.
Example: Find the median of {10, 40, 20, 50}.
- Order them: 10, 20, 40, 50
- Identify the middle pair: 20 and 40.
- Take the average of the pair: (20 + 40) / 2 = 30.
- Median = 30.
2. The Position Formula
If you have a large dataset (like 100 numbers) and want to know which position holds the median, use this formula where n is the number of items:
For example, if you have 99 numbers, the median is at position (99+1)/2 = 50. You would count to the 50th number.
3. Median vs. Mean: Why It Matters
[Image of mean vs median skewed distribution]The median is preferred over the mean when the data contains Outliers (extreme values).
Real-World Example (Home Prices):
- Imagine a neighborhood where 5 houses cost $100k, $100k, $100k, $100k, and $10 Million.
- The Mean would be over $2 Million. This is misleading; the "average" person cannot afford a $2M house there.
- The Median would be $100k. This accurately tells you what a typical house costs.
Conclusion
The Median is a powerful statistical tool for finding the center of a group. By ignoring the noise of extreme outliers, it often gives a more honest picture of what is "normal" in datasets regarding income, home prices, and test scores.