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Statistical Analyzer

Paste numbers to get a complete statistical summary: mean, median, mode, standard deviation, variance, quartiles (Q1/Q2/Q3), IQR, skewness, and kurtosis.

Detailed statistics (text)
 
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How to use Statistical Analyzer

  1. Paste numbers separated by commas, spaces, or newlines.
  2. Review the card grid with all computed statistics.
  3. Expand the detailed text output to copy or save results.

What is Statistical Analyzer?

This statistical analyzer gives you a complete summary of any numeric dataset. It computes the most commonly used descriptive statistics — from basic measures like count, sum, and range to advanced ones like standard deviation (both population σ and sample s), coefficient of variation, quartiles, interquartile range, skewness, and kurtosis.

Everything runs client-side in your browser — your data never leaves your device. Numbers can be separated by commas, spaces, semicolons, or newlines.

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FAQ

What's the difference between population and sample standard deviation?
Population standard deviation (σ) divides by N and is used when your data represents the entire population. Sample standard deviation (s) divides by N-1 and is used when your data is a sample from a larger population — it gives an unbiased estimate.
What does skewness tell me?
Skewness measures asymmetry in your distribution. Positive skew means the tail extends right (more values below the mean), negative skew means the tail extends left. Values between -0.5 and 0.5 indicate roughly symmetric data.
What is kurtosis?
Kurtosis measures the 'tailedness' of your distribution. Excess kurtosis (where normal = 0) tells you if your data has heavier tails (leptokurtic, > 0) or lighter tails (platykurtic, < 0) than a normal distribution.
Is my data uploaded to a server?
No. All statistics are computed entirely in your browser. Your numbers never leave your device.

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Author

SC
Sarah Chen"The Data Whisperer"

Data Analyst & CSV Specialist

Sarah has spent more than a decade wrangling spreadsheets, debugging CSV exports, and teaching teams how to make data work for them. She previously led data-quality initiatives at a fintech unicorn, where she reduced reporting errors by 40 percent through automated validation pipelines. When she is not optimizing queries or writing about data tooling, you will find her mentoring junior analysts at local bootcamps and speaking at data literacy meetups.

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