Mean/Median/Mode Calculator
Calculate basic statistics of data.
Input Examples
- • Comma separated: 1, 2, 3, 4, 5
- • Space separated: 1 2 3 4 5
- • Line separated: Enter each number on a new line
- • Mixed formats allowed
Mean
The sum of all values divided by the number of data points. Also called arithmetic average.
Mean = Sum ÷ Count
Median
The middle value when data is arranged in order from smallest to largest.
Odd count: Middle value
Even count: Average of two middle values
Mode
The value that appears most frequently in the data set. There can be multiple modes.
Range
The difference between the maximum and minimum values, indicating the spread of the data.
Range = Maximum - Minimum
From Ancient Times to Modern Era
The origins of statistics trace back to ancient Egyptian and Babylonian censuses. Modern descriptive statistics began with John Graunt's life table research in the 17th century and developed further when Adolphe Quetelet introduced the concept of the 'average man' in the 19th century.
Descriptive Statistics in the Data Science Era
- • EDA: Exploratory Data Analysis (EDA): Understanding basic data characteristics
- • Data Preprocessing: Data preprocessing: Outlier detection and data quality assessment
- • Feature Engineering: Feature engineering: Foundation for creating new variables
- • Model Evaluation: Model performance evaluation: Analyzing distribution characteristics of predictions
Applications in Business Intelligence
Modern businesses use descriptive statistics extensively across all areas including KPI dashboards, customer segmentation, sales analysis, and quality control. They play a crucial role especially in real-time analysis and automated reporting.
Mean Applications
- • Calculate average customer purchase amount
- • Analyze average website session duration
- • Predict average product lifespan
- • Measure average employee productivity
Median Applications
- • Income distribution analysis (minimizing outlier effects)
- • Real estate price analysis
- • Response time performance metrics
- • Customer satisfaction median values
Mode Applications
- • Identify most popular products
- • Analyze customer preference categories
- • Error code frequency analysis
- • Survey response patterns
Range Applications
- • Set quality control tolerance ranges
- • Determine pricing policy ranges
- • Performance evaluation grade intervals
- • Resource usage variation ranges
Development to Inferential Statistics
Descriptive statistics form the foundation of inferential statistics. Sample descriptive statistics are used to estimate population characteristics and serve as basic data for hypothesis testing.
Connection with Machine Learning
Descriptive statistics are essential for feature selection, data preprocessing, and model interpretation in machine learning. They play an important role especially in explainable AI.
Integration with Visualization
Combined with various visualization techniques such as histograms, box plots, and violin plots, they enable intuitive understanding of data distribution and characteristics.
Future of Real-time Analysis
Technology for calculating and updating statistics in real-time from streaming data environments is becoming increasingly important.