Calculator Suite

Statistical Analysis Hub

Professional-grade statistical calculators for research, analysis, and data science

5

Statistical Tests

1000+

Max Dataset Size

CSV

Import/Export

Available Calculators

Choose the right statistical test for your analysis

Descriptive StatisticsMost Popular
Beginner

Calculate mean, median, mode, standard deviation, and other summary measures for your dataset.

Common Use Cases:

Data summarizationDistribution analysisOutlier detection

Sample Size:

Any size (3+ recommended)

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T-Test Calculator
Intermediate

Perform one-sample, two-sample, and paired t-tests to compare means and test hypotheses.

Common Use Cases:

Compare group meansBefore/after studiesQuality control

Sample Size:

Small to medium (30+ ideal)

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Correlation Analysis
Intermediate

Measure relationships between variables using Pearson, Spearman, and Kendall correlations.

Common Use Cases:

Relationship strengthVariable associationPredictive modeling prep

Sample Size:

Medium to large (30+ recommended)

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Linear Regression
Advanced

Build predictive models and analyze relationships with residual analysis and confidence intervals.

Common Use Cases:

Prediction modelingTrend analysisCause-effect relationships

Sample Size:

Medium to large (50+ recommended)

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Chi-Square Test
Intermediate

Test independence and goodness of fit for categorical data and frequency distributions.

Common Use Cases:

Categorical data analysisIndependence testingDistribution fitting

Sample Size:

Large (100+ recommended)

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Choosing the Right Test

For Data Summary:

Start with Descriptive Statistics to understand your data distribution.

Comparing Groups:

Use T-Tests for comparing means between 1-2 groups.

Relationships:

Correlation Analysis measures how variables relate to each other.

Prediction:

Linear Regression helps predict one variable from another.

Categories:

Chi-Square Tests work with categorical data and frequencies.

Data Preparation

Before You Begin:

  • • Clean your data (remove duplicates, handle missing values)
  • • Check for outliers that might skew results
  • • Ensure adequate sample size for your chosen test
  • • Understand your data types (continuous vs categorical)
  • • Consider the assumptions of each statistical test

CSV Import Tips:

  • • First row should contain column headers
  • • Use consistent data formats (dates, numbers)
  • • Avoid special characters in column names
  • • Maximum 1000 rows for optimal performance
Understanding Results

P-Values:

  • • p < 0.05 = Statistically significant
  • • p < 0.01 = Highly significant
  • • p ≥ 0.05 = Not statistically significant

Effect Sizes:

  • • Small effect: 0.2 - 0.5
  • • Medium effect: 0.5 - 0.8
  • • Large effect: 0.8+
Need Help?

Each calculator includes detailed explanations, assumptions, and interpretation guidelines.

Start with Descriptive Statistics