Statistics Calculators

Professional statistical analysis tools for researchers, data scientists, and students. Perform hypothesis tests, calculate descriptive statistics, analyze variance, measure accuracy, and conduct comprehensive statistical analysis with instant results and detailed interpretations.

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All Statistics Calculators

Statistical Analysis Categories

Hypothesis Testing

Compare group means, test statistical significance, and analyze variance with comprehensive hypothesis testing tools.

Accuracy Metrics

Calculate precision, recall, F1 score, and 15+ classification metrics for machine learning and diagnostic testing.

Descriptive Stats

Calculate mean, median, mode, standard deviation, and comprehensive descriptive statistics for data analysis.

Statistics Quick Reference

Statistical Significance Levels

p < 0.01

Highly Significant

Strong evidence against null hypothesis. Less than 1% chance of random occurrence.

p < 0.05

Significant

Standard threshold. Less than 5% chance results occurred by chance.

p < 0.10

Marginally Significant

Weak evidence. Used in exploratory research but requires caution.

p ≥ 0.10

Not Significant

Insufficient evidence. Cannot reject null hypothesis with confidence.

Common Statistical Tests

Variance Analysis

Compare multiple group means and test for statistical differences across datasets.

Hypothesis Testing

Test null hypotheses and determine statistical significance of your research findings.

Distribution Analysis

Analyze data distributions, test normality, and evaluate statistical properties.

Correlation & Regression

Measure relationships between variables and predict outcomes with regression models.

Statistics Best Practices

Sample Size Matters

  • Aim for 20-30 samples per group minimum
  • Small samples (<10) reduce statistical power
  • Large samples (50+) detect tiny differences
  • Balance practical constraints with power needs

Check Assumptions

  • Verify data normality with histograms or Q-Q plots
  • Test homogeneity of variance (equal spreads)
  • Ensure independence of observations
  • Use non-parametric tests if assumptions fail

Report Correctly

  • Always report test statistic and p-value
  • Include effect sizes (eta-squared, Cohen's d)
  • Provide confidence intervals when possible
  • Describe practical significance, not just statistical