*The difference between mean and median is that mean is the average of a set of numbers, while the median is the middle value when the numbers are arranged in order. Mean is influenced by extreme values, while median is more resistant to them.*

**What Is Mean?**

In mathematics and statistics, the “mean” is a measure of central tendency that represents the average value of a set of numbers. It is calculated by adding up all the numbers in the set and then dividing by the total count of numbers. The mean provides a sense of the “typical” value within the dataset.

**Example of Mean**

Let’s take a set of exam scores: 85, 92, 78, 88, and 95. To find the mean, you would add up all these scores and then divide by the total count (which is 5 in this case):

Mean = (85 + 92 + 78 + 88 + 95) / 5 = 87.6

**Result**

So, the mean exam score for this set of students is 87.6.

**What Is Median?**

The “median” is a statistical measure that represents the middle value in a dataset when the numbers are arranged in ascending or descending order. It is not affected by extreme values, making it useful for describing the “central” value of a distribution.

If the dataset has an odd number of values, the median is the middle number. If the dataset has an even number of values, the median is the average of the two middle numbers.

**Example Of Median**

Let’s consider the following dataset of exam scores: 78, 85, 88, 92, and 95.

First, we arrange the scores in ascending order: 78, 85, 88, 92, 95.

Since the dataset has an odd number of values (5), the median is the middle number, which is 88.

**Result**

So, in this case, the median exam score is 88.

**Mean Vs Median**

The basic difference between mean and median is that:

Aspect | Mean | Median |

Definition | Average value of the dataset | Middle value in the dataset |

Calculation | Sum of all values divided by total count of values | Middle value when data is ordered |

Sensitivity | Sensitive to extreme values | Not affected by extreme values |

Usefulness | Useful for describing overall average | Useful for describing central value, especially with skewed data |

Influence of Outliers | Affected by outliers | Not strongly influenced by outliers |

Calculation Complexity | Can be affected by complex distributions | Simpler to calculate for skewed data |