Descriptive statistics calculator
Type or paste a data set —an Excel column too, with a decimal point or comma— and get every usual measure of a statistics course, with the formulas and the steps.
What you can do
- Central tendency: mean, median, mode, geometric mean and harmonic mean.
- Dispersion: range, sample (n − 1) and population (N) variance and standard deviation, coefficient of variation, standard error and mean absolute deviation.
- Position: quartiles, interquartile range and percentiles, by linear interpolation as in Excel.
- Shape: skewness and kurtosis; outliers by the Tukey fences.
- Data with frequencies, absolute, relative and cumulative frequency table, histogram and box plot.
Guide: descriptive statistics step by step
Descriptive statistics summarize a data set with a few numbers: where its center is, how spread out it is and what shape its distribution has. This guide explains each measure, when to use it and works a complete example by hand.
Measures of central tendency
- Mean (x̄): the sum of the data divided by n. It is sensitive to extreme values.
- Median: the middle value of the sorted data (with even n, the average of the two middle ones). Extreme values do not affect it.
- Mode: the most frequent value; there can be several or none.
If the mean and the median are far apart, the distribution is skewed or there are outliers.
Measures of spread
- Range: maximum minus minimum.
- Variance: the average of the squared deviations from the mean. The sample variance divides by n − 1 and the population variance by n. With a sample, to estimate the variability of the whole population, use n − 1.
- Standard deviation: the square root of the variance, in the same units as the data.
- Coefficient of variation: CV = 100 · s / x̄. It lets you compare the spread of data on different scales.
- Standard error of the mean: SE = s / √n. It measures how much the mean would vary from one sample to another.
Quartiles and outliers
Quartiles split the sorted data into four parts: Q1 has 25 % of the data below it and Q3, 75 %. The interquartile range is IQR = Q3 − Q1. There are several methods for computing quartiles, and with few data they do not all agree. The tool uses Excel's PERCENTILE.INC / QUARTILE.INC: the position of quantile p is 1 + p·(n − 1) in the sorted data, interpolating between neighbors. By Tukey's rule, a value is an outlier if it lies below Q1 − 1.5·IQR or above Q3 + 1.5·IQR.
Worked example
Data: 4, 8, 6, 5, 3, 7, 9, 5 (n = 8).
- Sorted: 3, 4, 5, 5, 6, 7, 8, 9.
- Mean: 47 / 8 = 5.875.
- Median: (5 + 6)/2 = 5.5. Mode: 5. Range: 9 − 3 = 6.
- Squared deviations: their sum is Σ(x − x̄)² = 28.875.
- Sample variance: 28.875 / 7 = 4.125; standard deviation s = √4.125 ≈ 2.031. (The population values would be 28.875/8 ≈ 3.609 and σ ≈ 1.900.)
- CV = 100 · 2.031 / 5.875 ≈ 34.6 %; SE = 2.031 / √8 ≈ 0.718.
- Quartiles: Q1 is at position 1 + 0.25·7 = 2.75, between the 2nd value (4) and the 3rd (5): Q1 = 4 + 0.75·(5 − 4) = 4.75. Q3 is at position 6.25: Q3 = 7 + 0.25·(8 − 7) = 7.25. IQR = 2.5.
- Outliers: the fences are 4.75 − 3.75 = 1 and 7.25 + 3.75 = 11; no value falls outside.
The calculator gives the same values, plus the skewness and kurtosis, adjusted as in Excel (here, 0.224 and −0.886).
How to use the calculator
- Type or paste the data. If a value repeats many times, you can give its frequencies separately, in the same order. Choose the decimal separator and whether the data are a sample or the whole population.
- Read the measures of center, spread, position (quartiles and percentiles) and shape, the outliers, the histogram, the box plot and the frequency table.
- Go through the steps to see each calculation: sorting, summing, the deviations table and the quartile interpolation. The detailed PDF solution has the full derivation.
Common mistakes
- Dividing by n when the data are a sample. It underestimates the population variance.
- Computing the median without sorting the data.
- Comparing quartiles computed with different methods: your textbook or calculator may use another rule.
- Summarizing very skewed data with the mean (incomes, waiting times); there the median represents the center better.
Further reading
D. C. Montgomery and G. C. Runger, Applied Statistics and Probability for Engineers (Wiley); R. E. Walpole, R. H. Myers, S. L. Myers and K. Ye, Probability and Statistics for Engineers and Scientists (Pearson). Simuveliq is not affiliated with these authors or publishers.
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