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Calculate the average (mean) along with median and mode for comprehensive statistical analysis.
The average (arithmetic mean) is the most commonly used measure of central tendency. It represents the typical value by adding all numbers and dividing by the count. This calculator also shows median and mode for a complete statistical picture.
The sum divided by the count. Most sensitive to outliers.
The middle value when sorted. Resistant to outliers.
The most frequent value. Can have multiple modes or none.
| Measure | Best For | Avoid When |
|---|---|---|
| Mean | Symmetric data, normal distributions | Outliers present, skewed data |
| Median | Skewed data, income, house prices | Small datasets, need all data points |
| Mode | Categorical data, finding typical | Continuous data, all values unique |
Distorted by the $2M mansion - not representative
Best measure - represents typical house price
All prices are unique
In everyday usage, yes. 'Average' usually refers to the arithmetic mean. However, technically 'average' can refer to any measure of central tendency (mean, median, or mode). When people say 'average,' they almost always mean the arithmetic mean.
Each measure reveals different aspects of your data. Comparing them helps identify skewness and outliers. If mean and median differ significantly, your data is skewed. If they're similar, your data is roughly symmetric.
Add all percentages and divide by count: (85% + 90% + 78%) ÷ 3 = 84.3%. Note: this is only correct if each percentage represents equal weight. For weighted percentages, use the weighted average calculator.
No, the average (mean) must be between the minimum and maximum values in your dataset. If calculated correctly, it cannot be larger than the largest number or smaller than the smallest number.
The average should be somewhere in the middle of your numbers. Quick check: it should be less than the largest and greater than the smallest. Also, multiply your average by the count - it should equal the sum of your numbers.
Generally, use one more decimal place than your original data. If your numbers are whole (like test scores), 1-2 decimals is usually enough. For precise scientific work, use as many as needed for accuracy.