Summary Statistics Calculator Online – Free | 8gwifi.org

Summary Statistics Calculator

Complete descriptive statistics analysis: mean, median, mode, SD, variance, quartiles, skewness, kurtosis

Data Input
Enter numbers separated by commas, spaces, or newlines
Understanding Summary Statistics
Measures of Central Tendency
  • Mean (Average): Sum of all values divided by count. Sensitive to outliers.
  • Median: Middle value when sorted. Resistant to outliers.
  • Mode: Most frequently occurring value(s). Can have multiple modes or none.
Measures of Dispersion
  • Range: Max - Min. Simple measure of spread.
  • Variance (σ²): Average squared deviation from mean. In squared units.
  • Standard Deviation (σ): Square root of variance. In original units.
  • Coefficient of Variation (CV): (SD / Mean) × 100%. Relative variability.
Distribution Shape
  • Skewness: Measures asymmetry
    • 0 = Symmetric (normal distribution)
    • > 0 = Right-skewed (tail on right)
    • < 0 = Left-skewed (tail on left)
  • Kurtosis: Measures tailedness
    • 3 = Normal distribution (mesokurtic)
    • > 3 = Heavy tails (leptokurtic)
    • < 3 = Light tails (platykurtic)
Quartiles
  • Q1 (25th percentile): 25% of data below this value
  • Q2 (50th percentile): Median - divides data in half
  • Q3 (75th percentile): 75% of data below this value
  • IQR (Q3 - Q1): Spread of middle 50% of data
When to Use
  • Data Exploration: Initial analysis of any dataset
  • Report Writing: Standard descriptive statistics section
  • Quality Control: Monitor process stability
  • Comparison: Compare different datasets or groups
  • Assumption Checking: Verify normality for parametric tests
Results

Enter your data and click calculate to see complete summary statistics

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Summary Statistics: FAQ

What are the most useful summaries?

Report central tendency (mean/median), spread (SD/IQR), shape (skewness/kurtosis) and range/five‑number summary depending on your data and audience.

Mean vs median — which to trust?

Median is robust for skewed/heavy‑tailed data; mean is efficient for symmetric distributions without outliers.

When to use IQR or SD?

Use IQR for robust spread (resistant to outliers). Use SD when the normal model is reasonable or you need variance‑based methods.

How to deal with outliers?

Investigate first. Consider robust stats (median/IQR), transformations, or trimming only with justification.

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