Statistics Calculator

Calculate comprehensive statistics including mean, median, mode, standard deviation, variance, quartiles, percentiles, and more.

For learning and homework help — verify critical calculations independently.

Reviewed by CalculatorDrive Math Editorial Board · Last updated

Calculator

Enter numbers separated by commas, spaces, or newlines to calculate comprehensive statistics.

The short answer

This calculator returns a full statistical summary in one pass: mean and median for center, mode for the most frequent value, range and standard deviation for spread, and quartiles (Q1, Q2, Q3) plus the interquartile range (IQR) for a spread measure that resists outliers. Together, these numbers describe a data set's center, spread, and shape without needing to look at every individual value.

Key takeaways

  • Mean, median, and mode each describe "center" differently — comparing them reveals whether data is symmetric or skewed.
  • IQR (Q3 − Q1) captures the spread of the middle 50% of the data and is far less sensitive to outliers than range or standard deviation.
  • Skewness describes asymmetry: right-skewed data (mean > median) has a long tail toward high values; left-skewed (mean < median) has a long tail toward low values.
  • No single statistic tells the whole story — a full summary of center, spread, and shape together prevents misreading a data set from one number alone.

Reading a full statistical summary together

Statistic What it tells you
MeanArithmetic center, sensitive to outliers
MedianMiddle value, resistant to outliers
ModeMost frequent value
RangeFull spread (max − min), very sensitive to outliers
Standard deviationTypical distance from the mean
IQRSpread of the middle 50%, resistant to outliers

Skewness: what mean vs. median reveals

Data: 20, 22, 23, 24, 25, 26, 28, 120 — one clear outlier at the end.

Mean = 288 ÷ 8 = 36

Median = (24 + 25) / 2 = 24.5

The mean (36) sits well above the median (24.5) — a clear sign of right skew. A single value of 120 was enough to drag the mean up by more than 11 points while the median barely moved, since the median only cares about which value is in the middle position, not how far the outlier sits from everything else.

Worked example: comparing IQR and range

Using the same data set (20, 22, 23, 24, 25, 26, 28, 120):

Range = 120 − 20 = 100

Q1 (median of lower half 20,22,23,24) = 22.5

Q3 (median of upper half 25,26,28,120) = 27

IQR = 27 − 22.5 = 4.5

The range (100) is dominated entirely by the single outlier, while the IQR (4.5) reflects how tightly packed the bulk of the data actually is — a dramatic illustration of why IQR is preferred for spread when outliers are a concern.

Common mistakes to avoid

  • Reading only the mean and assuming it represents a "typical" value — always check the median too, especially with small samples or suspected outliers.
  • Treating range as a reliable spread measure — a single extreme value can inflate it dramatically, as shown above.
  • Expecting mode on data with no repeated values — a set where every value is unique simply has no mode.
  • Misreading skew direction — right-skewed means the tail (and the mean) point toward higher values, not that most of the data is high.

Frequently Asked Questions

What summary statistics describe a data set?

Center (mean, median), spread (range, standard deviation), and shape (skewness) together give a fuller picture than any single number.

When should you use sample versus population formulas?

Use population formulas when you have every member of the group. Use sample formulas when data are a subset meant to represent a larger population.

What is skewness?

Skewness describes asymmetry. Right-skewed data have a long tail toward higher values; mean exceeds median. Left-skewed is the opposite.

Why report quartiles with median?

Quartiles (Q1, Q2, Q3) show spread in skewed data without being pulled by outliers the way mean and SD can be.

How do I use this statistics calculator?

Enter your data set, then click Calculate to see mean, median, mode, standard deviation, quartiles, and related summaries.

What does it mean if the mean is much higher than the median?

It is a sign of right (positive) skew — a small number of unusually high values are pulling the average upward while most data points sit lower. Household income is the classic example: a few very high earners pull the mean well above the median, so the median is usually the more representative "typical" value for skewed data like this.

More math calculators