Statistics Calculator

Enter a comma-separated list of numbers to compute descriptive statistics.

Count
0
Sum-
Mean (̄x)-
Median-
Mode-
Range-
Minimum-
Maximum-
Population Std Dev (σ)-
Sample Std Dev (s)-
Population Variance (σ2)-
Sample Variance (s2)-
Q1 (First Quartile)-
Q3 (Third Quartile)-
IQR (Interquartile Range)-

About Statistics Calculator

This Statistics Calculator computes a comprehensive set of descriptive statistics for any comma-separated numerical data set: count, sum, mean, median, mode, range, minimum, maximum, population and sample standard deviation, population and sample variance, first quartile (Q1), third quartile (Q3), and the interquartile range (IQR). Descriptive statistics summarize the central tendency, dispersion, and shape of a data distribution, providing a foundation for data analysis in any field. Use this tool for analyzing survey responses, tracking business metrics, evaluating student performance, processing sensor data, or exploring any collection of numbers. The median and IQR are robust statistics that resist the influence of outliers, while the mean and standard deviation are parametric measures that assume a roughly normal distribution. Quartiles help identify the spread of the middle 50% of data and are used in box plot visualizations. The mode reveals the most frequently occurring value. By presenting all these statistics together, this calculator gives you a complete statistical profile of your data set in one click, making it an essential tool for students, researchers, and data professionals.

How to Interpret Your Results

Central Tendency: The mean (89.78 in our example) is the average, but the median (90) sometimes tells a better story. If the mean is much higher than the median, your data has a rightward skew (a few very high values pulling the average up). If the mean is lower, the skew is leftward.

Spread: The range (22) tells you the full spread, but the IQR (13) is more useful because it ignores extreme values. A large standard deviation (like 7.46) relative to the mean means your data is very spread out. For instance, if the mean is 89.78 and σ = 7.46, about 68% of students scored between 82.32 and 97.24.

Quartiles: Q1 (83.5) means 25% of students scored below 83.5. Q3 (96.5) means 75% scored below 96.5. If you're aiming to be in the top 25%, you need above 96.5. The IQR of 13 tells you the spread of the middle half of your class.

How to Use This Calculator

Suppose you have test scores from a class: 78, 82, 85, 88, 90, 92, 95, 98, 100. Enter them as comma-separated values: "78, 82, 85, 88, 90, 92, 95, 98, 100" and click Calculate. The count is 9 students, the mean is 89.78, and the median is 90. The range is 22 (from 78 to 100), and the standard deviation (sample) is 7.46, showing moderate spread. Q1 is 83.5 and Q3 is 96.5, giving an IQR of 13 — the middle 50% of students scored between 83.5 and 96.5. Now try adding an outlier: change one score to 30. The mean drops dramatically to 82.22, but the median only shifts to 88, demonstrating how the median resists outliers. The standard deviation jumps to 22.4, reflecting the increased spread.

Frequently Asked Questions

What's the difference between population and sample standard deviation?

Population standard deviation (σ) divides by n and is used when your data includes every member of the group you're studying. Sample standard deviation (s) divides by n-1 (Bessel's correction) and is used when you have only a subset. For data 2, 4, 6, 8, 10, population σ ≈ 2.83 but sample s ≈ 3.16 — the sample version is slightly larger to account for uncertainty.

What is the difference between mean, median, and mode?

The mean is the arithmetic average (sum ÷ count). The median is the middle value when data is sorted. The mode is the most frequent value. For the dataset 2, 3, 3, 5, 7: mean = 4, median = 3, mode = 3. Mean is sensitive to outliers, median is robust, and mode identifies the most common value.

How do I find the range of a data set?

The range is the difference between the maximum and minimum values in a dataset. For example, if test scores are 65, 72, 85, 90, 98, the range is 98 - 65 = 33. Range gives a quick sense of spread but is highly sensitive to outliers. The IQR (interquartile range) is often more informative.

What is the interquartile range?

The interquartile range (IQR) is the difference between the third quartile (Q3, 75th percentile) and the first quartile (Q1, 25th percentile). It represents the middle 50% of the data and is resistant to outliers. For example, if Q1 = 83.5 and Q3 = 96.5, the IQR = 13, meaning the middle half of data spans 13 units.

When should I use median instead of mean?

Use the median when your data has outliers or is skewed, because the median is resistant to extreme values. For example, in salary data with a few executives earning millions, the median better represents the typical worker's salary. Use the mean when data is symmetrically distributed without significant outliers.