Mean, Median & Mode Calculator
Enter any dataset and instantly get the mean, median, mode, range, quartiles and full frequency distribution.
Separate values with commas, spaces or new lines.
■ Purple = Mode (appears 3 times)
| Value | Frequency | Rel. Freq. |
|---|---|---|
| 2 | 1 | 12.5% |
| 3 | 2 | 25.0% |
| 4 | 1 | 12.5% |
| 7 | 3 | 37.5% |
| 9 | 1 | 12.5% |
Why "Average Salary" Headlines Almost Always Mislead You
Suppose a company has 9 employees earning ₹30,000 per month and one CEO earning ₹3,00,000. The mean salary is (9 × 30,000 + 3,00,000) / 10 = ₹57,000. The headline reads "average employee earns ₹57,000" — technically correct, but 90% of employees earn far less than that. The median — the middle value when sorted — is ₹30,000, which actually reflects what most people there are taking home. This gap between mean and median is not a calculation error; it is what happens when distributions are skewed by extreme values. Income, wealth, company sizes, and city populations all have this property.
This free mean, median and mode calculator computes all three measures of central tendency simultaneously, along with range, quartiles, IQR, and a complete frequency distribution table. Enter your data once, get the full picture instantly — and see which measure actually represents your dataset.
When Mode Matters More Than Mean or Median
For a shoe manufacturer deciding which sizes to stock most heavily, the mean shoe size is useless — shoe sizes are discrete categories, not a continuous measurement where averaging makes sense. The mode (most common size) is the only meaningful average here. The same applies to survey responses ("rate your satisfaction 1–5"), blood types in a hospital blood bank, and the most common number of items in a shopping cart. Mode is the only measure of central tendency that works on categorical data.
The sorted data display in this tool highlights mode values in purple so you can spot them immediately in context. A bimodal distribution — one with two distinct modes — often signals two separate subgroups in the data. A class where half the students score around 40% and the other half around 80% shows up as bimodal, suggesting the teaching approach is not reaching one group. Recognising that shape from the mode values is the first diagnostic step.
IQR — The Outlier-Resistant Spread Measure
Range (maximum minus minimum) tells you the total spread of data, but a single extreme value can make it meaningless. If 29 students score between 60 and 90 on a test but one student scores 15, the range jumps to 75 even though the bulk of the class is in a 30-point band. The Interquartile Range (IQR = Q3 − Q1) measures the spread of the middle 50% of the data, completely ignoring the top and bottom quarters. It is resistant to outliers by design.
The standard outlier detection rule uses IQR directly: any value below Q1 − 1.5×IQR or above Q3 + 1.5×IQR is a potential outlier — the same rule that draws the whiskers in a box-and-whisker plot. For complete variability analysis including standard deviation and variance, use this tool together with the Standard Deviation Calculator. All calculations run in your browser — no data is transmitted anywhere.
✓Verified by ToollyX Team · Last updated June 2026