Descriptive Statistics summarize and describe key features of data. The most commonly used measures are Mean, Median, Variance, and Standard Deviation. These provide a snapshot of the dataset’s central tendency and spread.
The Mean is the arithmetic average of values in a dataset.
Formula: Mean = (Σx) / n
If 5 employees earn $20k, $22k, $25k, $28k, and $100k, the mean = (20+22+25+28+100) ÷ 5 = $39k. But this is misleading due to the $100k outlier.
The Median is the middle value when numbers are arranged in order.
For the same salaries ($20k, $22k, $25k, $28k, $100k), the median = $25k. This gives a more realistic view of employee pay.
Variance measures how spread out the values are from the mean. A high variance indicates values are widely spread; low variance means they are close together.
Formula: Variance = Σ(x − Mean)² / n
Two sales teams both average $50k monthly sales. Team A’s sales vary between $49k–$51k (low variance). Team B’s sales vary between $20k–$80k (high variance).
Standard Deviation is the square root of variance. It is easier to interpret because it’s in the same unit as the data.
Formula: Std Dev = √Variance
If average delivery time = 30 mins, a Std Dev of 2 mins means most deliveries are consistent. A Std Dev of 10 mins means high unpredictability.