Find mean, median, and mode in one step to compare average, middle value, and most frequent value.
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Mean is the arithmetic average. Median is the middle value when sorted. Mode is the most frequent value. Use median for skewed data (incomes, house prices); mean for symmetric data; mode for categorical data.
The mean is sensitive to outliers — a single very large or small value shifts it significantly. The median is often a better central measure for skewed data (incomes, house prices, reaction times).
Results
Mean (sum / count): 2.4000
Median (middle value): 2.0000
Mode (most frequent value): 2
Mean, median, and mode are the three measures of central tendency — each describes a 'center' of a dataset differently.
**Mean (average):** Sum all values and divide by the count. Affected by outliers — one very high value pulls the mean up significantly.
**Median:** Sort all values and pick the middle one (or average of two middle values for even-count datasets). Resistant to outliers — extremely useful for income, house prices, and healthcare data where a few very large values skew the mean.
**Mode:** The value that appears most often. A dataset can have no mode (all values unique), one mode (unimodal), or multiple modes (bimodal/multimodal). Used for categorical data (most popular color, most common age in a survey) and for detecting the 'typical' repeating value.
**Example** with [10, 10, 15, 20, 100]:
• Mean: (10+10+15+20+100) ÷ 5 = 31
• Median: 15 (middle value after sorting)
• Mode: 10 (appears twice)
The mean (31) is distorted by the outlier 100. The median (15) better represents a 'typical' value in this skewed dataset.
Type or paste a list of numbers separated by commas, spaces, or new lines. You can paste from a spreadsheet column.
The tool instantly shows mean, median, mode, count, sum, min, and max for your dataset.
If mean and median are close, your data is roughly symmetric. If mean is much higher than median, you have high outliers (right-skewed). If mean is lower than median, you have low outliers (left-skewed).
Use the copy button to paste mean, median, mode, and summary stats into a report, spreadsheet, or homework submission.
Input
Salaries: $40k, $42k, $45k, $48k, $50k, $850k (CEO)Output
Mean: $179k · Median: $46.5k · Mode: noneThe CEO salary drags the mean to $179k — nearly 4x the typical employee salary. The median ($46.5k) is far more representative of what most employees actually earn.
Common real-world scenarios where this tool saves time.
The US median household income (~$80,000) is more representative than the mean (~$105,000) because the mean is pulled up by very high earners. When reporting 'average' income, median is almost always more informative.
Use mean for grades when data is roughly normally distributed. Use median to report 'typical' performance when a few very low scores would distort the mean unfairly.
Median home price is used by industry reports (Zillow, NAR) because a few luxury sales would inflate the mean significantly. Comparing mean and median reveals whether a market has many high-end outliers.
Mode tells you the most common response in a rating survey (most popular score). If mode and median diverge, you may have a bimodal distribution — two distinct groups responding very differently.
Choosing the right central tendency measure.
| Measure | Sensitive to outliers | Use for | Example use case |
|---|---|---|---|
| Mean | Yes — pulled by extremes | Symmetric, normal distributions | Student test scores, heights |
| Median | No — robust to outliers | Skewed data, outliers present | Income, house prices, wait times |
| Mode | No | Categorical or repeating data | Most popular product, most common score |
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Mean is the arithmetic average (sum ÷ count). Median is the middle value of the sorted dataset. Mode is the value that appears most often. For symmetric data, all three are equal (or close). For skewed data (most real-world data), they diverge — median is usually the most useful central measure.
Use median when your data has outliers or is skewed. Classic examples: income (a few billionaires distort the mean), house prices, response times, hospital stays, and customer order values. If the mean is much higher than the median, your data is right-skewed with high outliers.
If all values in a dataset are unique (appear exactly once), there is no mode — the dataset is amodal. The mode is only meaningful when some values repeat. For continuous measurements (like precise weights or times), mode is rarely useful because repeat values are uncommon.
Yes. A bimodal dataset has two values that both appear the same (highest) number of times. Example: [1, 1, 2, 3, 3] — mode is both 1 and 3. A bimodal distribution often indicates two distinct groups within your data — worth investigating.
For an even count, the median is the average of the two middle values. Example with [3, 5, 7, 10]: median = (5 + 7) ÷ 2 = 6. The median does not have to be a value that actually appears in the dataset.
Both are reported depending on data distribution. For normally distributed data (many physical measurements), mean ± standard deviation is standard. For non-normal or ordinal data (Likert scales, incomes, reaction times), median with interquartile range (IQR) is preferred. Good papers report both and discuss skewness.
In a perfectly symmetric distribution: mean = median = mode. In a right-skewed (positive skew) distribution: mean > median > mode. In a left-skewed (negative skew) distribution: mean < median < mode. You can estimate skewness from the relative position of mean and median without a formal test.
Dataset entries and statistical results are calculated locally in your browser—they are not uploaded to EverydayTools servers.
For statistical inference, also calculate standard deviation and consider the distribution shape.
More free tools for the same workflow.
Free average calculator: arithmetic mean, sum, count, min, max, and range from pasted lists—grades, budgets, or datasets. Private, instant, no signup. Runs locally in your browser when supported—no upload required for normal use.
Calculate population or sample standard deviation, see variance, and understand dataset spread with a simple variance-to-standard-deviation workflow. Runs locally in your browser when supported—no upload required for normal use.
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Reviewed on 2026-06-08.