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Average Calculator

Calculate mean, median, mode, standard deviation and more from a list of numbers.

Numbers (comma, space or newline separated)i
6 numbers entered
Mean
18
Median
15.5
Mode
4, 8, 15, 16, 23, 42
Std deviation
12.31530213
Min
4
Max
42
Sum
108
Count
6

Mean, median and mode: when to use each

Three measures of "average" describe the centre of a data set differently. Choosing the wrong one can seriously misrepresent what the data shows. A single extreme value (outlier) can drag the mean far from the typical value, making the median a better choice for skewed distributions like house prices or salaries.

Mean (arithmetic average)
Sum Γ· Count
Use for: Symmetric data without outliers. Exam scores, heights, temperatures.
⚠ Pulled by extreme values
Median (middle value)
Middle of sorted list
Use for: Skewed data with outliers. Salaries, house prices, response times.
⚠ Requires sorting first
Mode (most frequent)
Value with highest frequency
Use for: Categorical data, shoe sizes, survey responses.
⚠ Can have multiple modes or none

Standard deviation explained

Standard deviation (Οƒ) measures how spread out the values are around the mean. A small Οƒ means values cluster tightly around the mean; a large Οƒ means they are spread widely. It is calculated as the square root of the variance β€” the average of the squared distances from the mean.

Data: 2, 4, 4, 4, 5, 5, 7, 9 Mean = (2+4+4+4+5+5+7+9) Γ· 8 = 5 Variance = [(2βˆ’5)Β²+(4βˆ’5)Β²+(4βˆ’5)Β²+(4βˆ’5)Β²+(5βˆ’5)Β²+(5βˆ’5)Β²+(7βˆ’5)Β²+(9βˆ’5)Β²] Γ· 8 = [9+1+1+1+0+0+4+16] Γ· 8 = 4 Standard deviation Οƒ = √4 = 2 β†’ Most values fall within 5 Β± 2 (i.e. between 3 and 7)

The 68-95-99.7 rule (normal distribution)

In a normal (bell-curve) distribution, standard deviation defines predictable ranges: approximately 68% of values fall within Β±1Οƒ of the mean, 95% within Β±2Οƒ, and 99.7% within Β±3Οƒ. This rule is used in quality control, finance, and scientific research to judge whether an observation is unusual.

Β±1Οƒ
68.3%
Β±2Οƒ
95.4%
Β±3Οƒ
99.7%

Real-world examples

Salary reporting
Median salary is more informative than mean when a few executives earn disproportionately high pay
Quality control
Standard deviation measures production consistency β€” low Οƒ means fewer defects
Finance
Portfolio volatility is measured as Οƒ of returns; higher Οƒ = higher risk and potential reward
Sports analytics
Mean score vs. median helps identify whether an athlete is consistently good or occasionally brilliant
Medicine
Blood pressure readings: mean tells average level; Οƒ tells how stable it is over time
Education
Class average (mean) alongside Οƒ shows whether most students clustered near average or had polarised results

Tips for interpreting averages

  • Always report mean and standard deviation together. A mean of 50 tells you the centre; a Οƒ of 2 vs. Οƒ of 20 tells you completely different stories about the data.
  • Check for skewness. If mean and median differ substantially (e.g. mean $65K vs. median $45K salary), the distribution is skewed. The median is the more honest measure of "typical."
  • Sample vs. population standard deviation. This calculator uses population Οƒ (divide by n). If your data is a sample from a larger population, statistical software divides by nβˆ’1 (Bessel's correction) for a less biased estimate.

Frequently asked questions

What is the difference between mean, median and mode?

The mean is the sum divided by the count, the median is the middle value when sorted, and the mode is the most frequent value. Each describes the data differently.

When should I use the median instead of the mean?

Use the median when data has outliers or is skewed β€” for example incomes β€” because a few extreme values can distort the mean.

Can a data set have more than one mode?

Yes. If two values tie for most frequent, the set is bimodal; more than two makes it multimodal. A set with no repeats has no mode.

What is the range?

The range is the difference between the largest and smallest values, giving a quick sense of how spread out the data is.

iFormula / How it works

Mean = sum Γ· count | Median = middle value | Mode = most frequent | Std dev = √(Ξ£(xβˆ’ΞΌ)Β²Γ·n)

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