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Sample Size & Power AnalysisDetermine optimal sample size for your study Normality TestTest if your data follows a normal distribution Tukey's HSD TestPost-hoc test for ANOVA comparisons Interactive Z-TableStandard normal distribution lookup Frequency TableFrequency distributions and histograms Bell Curve GeneratorCustomized normal distribution curves

Trusted across academia

20,000+ monthly usersCited in 15 peer-reviewed papersAPA 7 formatted output

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Hypothesis Testing Calculators

Choose the right statistical test for your data. Our interactive selector helps you find the appropriate test based on your research question and data characteristics, or browse the comprehensive table of tests with their assumptions and formulas.

Prefer a visual guide?View our interactive flow chart →

Test Name	Check	Test Statistic	Assumptions
Normality Test Parametric	Diagnostic	$\text{Shapiro-Wilk, K-S, Anderson-Darling}$	Independence
Outlier Detection Parametric	Diagnostic	$\text{Grubbs, Dixon's Q, Isolation Forest}$	Independence
ANCOVA Parametric	Mean	$F = \frac{MS_{adj}}{MSE_{adj}}$	Normality Independence Equal Variance Linearity of Covariate Homogeneity of Regression
Dunnett's Test Parametric	Mean	$d = \frac{\bar{x}_i - \bar{x}_c}{\sqrt{2MSE/n}}$	Normality Independence Equal Variance
Friedman Test Non-parametric	Mean	$\chi^2(df) = \frac{12}{bk(k+1)}\sum R_j^2 - 3b(k+1)$	Dependent Groups Ordinal Data
Games-Howell Test Parametric	Mean	$q = \frac{\bar{x}_i - \bar{x}_j}{\sqrt{\frac{s_i^2}{n_i} + \frac{s_j^2}{n_j}}}$	Normality Independence Equal Variance
Kruskal-Wallis Test Non-parametric	Mean	$H = \frac{12}{N(N+1)}\sum_{i=1}^k \frac{R_i^2}{n_i} - 3(N+1)$	Independence Ordinal Data
MANOVA Parametric	Mean	$\Lambda = \frac{\|\mathbf{E}\|}{\|\mathbf{H} + \mathbf{E}\|}$	Normality Independence Equal Variance
One Sample Z-Test Parametric	Mean	$z = \frac{\bar{x} - \mu_0}{\sigma/\sqrt{n}}$	Normality Independence Known σ
One Sample t-Test Parametric	Mean	$t = \frac{\bar{x} - \mu_0}{s/\sqrt{n}}$	Normality Independence Known σ
One Way ANOVA Parametric	Mean	$F = \frac{MSG}{MSE}$	Normality Independence Equal Variance
Paired t-Test Parametric	Mean	$t = \frac{\bar{d}}{s_d/\sqrt{n}}$	Normality Paired Data
Permutation Test Non-parametric	Mean	$p = \frac{\sum_{i=1}^{N} I(T^*_i \geq T_{obs})}{N}$	Independence Normality Equal Variance
Repeated Measures ANOVA Parametric	Mean	$F = \frac{MSG}{MSE}$	Normality Dependent Groups Sphericity
Scheffé Test Parametric	Mean	$F_s = \frac{(\bar{x}_i - \bar{x}_j)^2}{MSE(\frac{1}{n_i} + \frac{1}{n_j})}$	Normality Independence Equal Variance
Three-way ANOVA Parametric	Mean	$F = \frac{MSG}{MSE}$	Normality Independence Equal Variance
Tukey's HSD Test Parametric	Mean	$q = \frac{\bar{x}_i - \bar{x}_j}{\sqrt{MSE/n}}$	Normality Independence Equal Variance
Two Sample Z-Test Parametric	Mean	$Z = \frac{(\bar{x}_1 - \bar{x}_2) - (\mu_1 - \mu_2)}{\sqrt{\frac{\sigma_1^2}{n_1} + \frac{\sigma_2^2}{n_2}}}$	Normality Independence Known σ
Two Sample t-Test (Pooled variance) Parametric	Mean	$t = \frac{\bar{x}_1 - \bar{x}_2}{s_p\sqrt{\frac{1}{n_1} + \frac{1}{n_2}}}$	Normality Independence Known σ Equal Variance
Two Sample t-Test (Welch's) Parametric	Mean	$t = \frac{\bar{x}_1 - \bar{x}_2}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}}$	Normality Independence Known σ Equal Variance
Two Way ANOVA Parametric	Mean	$F = \frac{MSG}{MSE}$	Normality Independence Equal Variance
Welch's ANOVA Parametric	Mean	$W = \frac{\sum w_i(\bar{x}_i - \hat{\mu})^2 / (k-1)}{1 + \frac{2(k-2)}{k^2-1}\sum \frac{(1-w_i/\sum w_j)^2}{n_i - 1}}$	Normality Independence Equal Variance
Chi-Square Goodness of Fit Test Parametric	Proportion	$\chi^2 = \sum \frac{(O - E)^2}{E}$	Normality Independence
Chi-Square Test of Independence Parametric	Proportion	$\chi^2 = \sum \frac{(O - E)^2}{E}$	Normality Independence
Fisher's Exact Test Non-parametric	Proportion	$p = \frac{(a+b)!(c+d)!(a+c)!(b+d)!}{n!a!b!c!d!}$	Independence Fixed Row/Column Totals
One Sample Proportion Test Parametric	Proportion	$z = \frac{\hat{p} - p_0}{\sqrt{\frac{p_0(1-p_0)}{n}}}$	Independence Binomial Data
Two Sample Proportion Test Parametric	Proportion	$Z = \frac{\hat{p}_1 - \hat{p}_2}{\sqrt{\hat{p}(1-\hat{p})(\frac{1}{n_1} + \frac{1}{n_2})}}$	Independence Binomial Data
Dunn's Test Non-parametric	Rank	$z_{ij} = \frac{\bar{R}_i - \bar{R}_j}{\sqrt{\frac{N(N+1)}{12}(\frac{1}{n_i} + \frac{1}{n_j})}}$	Independence Ordinal Data
Mann-Whitney U Test Non-parametric	Rank	$Z = \frac{U - \mu_U}{\sigma_U}$	Independence Ordinal Data
Wilcoxon Signed Rank Test Non-parametric	Rank	$Z = \frac{W - \mu_W}{\sigma_W}$	Paired Data Ordinal Data

Descriptive Statistics Calculators

Choose from our comprehensive collection of descriptive statistics calculators for both quantitative and qualitative data analysis.

Central Tendency

Calculator	Formula	Description
Mean	$\bar{x} = \frac{\sum_{i=1}^n x_i}{n}$	Calculate arithmetic average of numerical data
Median	$\text{Middle value when ordered}$	Find the middle value in ordered data
Mode	$\text{Most frequent value}$	Identify most common value(s) in dataset
Geometric Mean	$\sqrt[n]{x_1 \times x_2 \times ... \times x_n}$	Calculate mean for multiplicative relationships
Harmonic Mean	$\frac{n}{\sum_{i=1}^n \frac{1}{x_i}}$	Calculate mean for rates and speeds

Variability

Calculator	Formula	Description
Standard Deviation	$s = \sqrt{\frac{\sum(x_i - \bar{x})^2}{n-1}}$	Measure average deviation from mean
Mean Absolute Deviation	$\text{MAD} = \frac{\sum \|x_i - \bar{x}\|}{n}$	Measure average deviation from mean
Variance	$s^2 = \frac{\sum(x_i - \bar{x})^2}{n-1}$	Measure spread of data points
Range	$\text{max}(x) - \text{min}(x)$	Calculate difference between largest and smallest values
IQR	$Q_3 - Q_1$	Calculate spread of middle 50% of data
Coefficient of Variation	$CV = \frac{s}{\bar{x}} \times 100\%$	Compare variability between datasets

Position

Calculator	Formula	Description
Percentiles	$P_k = \text{value at }k\text{th percentile}$	Find value at specified percentile
Z-Score	$z = \frac{x - \bar{x}}{s}$	Calculate standardized scores

Shape

Calculator	Formula	Description
Skewness	$\frac{\sum(x_i - \bar{x})^3}{(n-1)s^3}$	Measure asymmetry of distribution
Kurtosis	$\frac{\sum(x_i - \bar{x})^4}{(n-1)s^4}$	Measure tailedness of distribution

Relationships

Calculator	Formula	Description
Correlation Coefficient	$r = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum (x_i - \bar{x})^2 \sum (y_i - \bar{y})^2}}$	Measure linear relationship strength with Pearson's r

Probability Distribution Calculators

Access our suite of probability distribution calculators for both discrete and continuous random variables. Calculate probabilities, find critical values, and visualize distributions.

Common Discrete

Distribution	Probability Function	Description
Binomial	$P(X=k) = \binom{n}{k}p^k(1-p)^{n-k}$	Model number of successes in fixed trials
Poisson	$P(X=k) = \frac{\lambda^k e^{-\lambda}}{k!}$	Model rare events in fixed interval
Geometric	$P(X=k) = p(1-p)^{k-1}$	Model trials until first success
Negative Binomial	$P(X=k) = \binom{k-1}{r-1}p^r(1-p)^{k-r}$	Model trials until r successes
Hypergeometric	$P(X=k) = \frac{\binom{K}{k}\binom{N-K}{n-k}}{\binom{N}{n}}$	Model sampling without replacement

Chart Makers

Choose the right visualization based on your data type and analysis goals. Our tools help you create clear, effective visual representations of your data.

Categorical Data

Chart Type	Description	Best Used For	Example
Bar Chart	Display frequencies or counts for categories	Comparing categories, showing distributions	Product sales by category
Pie Chart	Show part-to-whole relationships	Displaying proportions, percentages	Market share by company
Funnel Chart	Visualize conversion rates and stages in a process	Process flows, conversion funnels	Sales funnel conversion rates
Tree Map	Display hierarchical data using nested rectangles	Hierarchical composition, proportional sizes	Market share by product category
Waterfall Chart	Visualize cumulative values, gains and losses	Financial analysis, breakdown visualization	Profit and loss breakdown

Numerical Data

Chart Type	Description	Best Used For	Example
Histogram	Display distribution of continuous data	Examining data distribution shape	Age distribution of customers
Box Plot	Show data distribution and outliers	Identifying outliers, comparing distributions	Test scores distribution
Violin Plot	Combine box plot with kernel density	Detailed view of data distribution	Income distribution by department
Dot Plot	Visualize individual data points and their distribution	Small datasets, simple distributions	Daily temperature readings
Stem-and-Leaf Plot	Show distribution while preserving individual values	Small to medium datasets, retaining data values	Test scores with individual values
Density Plot	Visualize data distribution using kernel density estimation	Smooth distribution visualization	Income distribution analysis
Bell Curve Graph Generator	Visualize normal distribution with mean and standard deviation	Normal distribution visualization	IQ score distribution
Q-Q Plot	Assess normality and compare data distributions	Testing normality assumptions	Checking if data follows normal distribution

Confidence Interval Calculators

Choose from our comprehensive collection of confidence interval calculators for estimating population parameters and analyzing differences between groups.

Population Parameters

Interval Type	Description	Best Used For	Example
Mean	Estimate population mean	Continuous data, normal distribution	Average customer spending ± margin of error
Proportion	Estimate population proportion	Binary outcomes, categorical data	Customer satisfaction rate ± margin of error
Standard Deviation	Estimate population variability	Process variation, quality control	Manufacturing tolerance limits

Regression Analysis Calculators

Choose from our collection of regression analysis tools for modeling relationships between variables and making predictions from your data.

Linear Regression

Model Name	Description	Example
Simple Linear Regression	Model relationship between two continuous variables	Height vs weight relationship
Multiple Linear Regression	Model with multiple predictors	House price prediction using area, location, age

Non-Linear Regression

Model Name	Description	Example
Quadratic Regression	Model curved relationships with quadratic terms	Projectile motion, optimal pricing models
Exponential Regression	Model exponential growth or decay patterns	Population growth, radioactive decay, compound interest

Classification Models

Model Name	Description	Example
Logistic Regression	Model binary outcomes	Customer churn prediction

Limited Dependent Variable Models

Model Name	Description	Example
Censored Regression (Tobit)	Fit Tobit models for censored continuous outcomes	Hours worked when many observations are zero

Multivariate Analysis Calculators

Analyze multiple variables simultaneously with our multivariate tools — reduce dimensions, classify groups, and quantify relationships across variable sets.

Dimensionality Reduction

Method	Description	Example
Principal Component Analysis (PCA)	Reduce dimensionality by finding directions of maximum variance	Compress 50 survey items into 3 latent dimensions
Exploratory Factor Analysis	Identify latent factors that explain observed correlations	Discover underlying personality traits from items
Confirmatory Factor Analysis	Test a hypothesized factor structure against your data	Confirm a 3-factor structure for a published instrument

Classification

Method	Description	Example
Discriminant Analysis (LDA/QDA)	Classify observations and project to maximize group separation	Predict species from morphological measurements

Relationships

Method	Description	Example
Canonical Correlation Analysis	Examine relationships between two sets of variables	Relate test battery to job-performance metrics

Survival Analysis Calculators

Analyze time-to-event data — model survival curves, compare groups, and quantify covariate effects on hazard rates with APA-formatted output.

Time-to-Event Analysis

Method	Description	Example
Kaplan-Meier Survival Analysis	Estimate survival probabilities over time with censoring	Patient survival after treatment over 5 years
Log-Rank Test	Compare survival curves between two or more groups	New drug vs. standard treatment survival
Cox Proportional Hazards Regression	Model hazard rates with covariates and produce hazard ratios	Effect of age and stage on cancer survival

Statistical Resources

Access our collection of statistical tables, interactive simulations, and reference materials to support your statistical analysis.

Distribution Tables

Resource	Description
Z-Table	Standard normal distribution critical values for Z-tests and standardized scores
T-Table	Student's t-distribution critical values, ideal for small sample tests with unknown population σ
Chi-Square Table	Chi-square distribution critical values used in goodness of fit and independence tests
F-Table	F-distribution critical values for ANOVA and variance comparisons
Wilcoxon Signed-Rank Table	Critical values for Wilcoxon signed-rank test and paired sample comparisons

Analyze Your Data with Confidence

Fast, accurate statistical analysis trusted by students and researchers

Statistics, no PhD required.

Free calculators with step-by-step solutions, APA-formatted output, and exports built for your thesis or paper.

Cmd+K

or jump to a popular tool below

Popular calculatorsSee all calculators →

Trusted across academia

20,000+ monthly usersCited in 15 peer-reviewed papersAPA 7 formatted output

NIH / PMCSpringerElsevierWileyBMJarXivbioRxivEU · JRC

Hypothesis Testing Calculators

Prefer a visual guide?View our interactive flow chart →

Test Name	Check	Test Statistic	Assumptions
Normality Test Parametric	Diagnostic	$\text{Shapiro-Wilk, K-S, Anderson-Darling}$	Independence
Outlier Detection Parametric	Diagnostic	$\text{Grubbs, Dixon's Q, Isolation Forest}$	Independence
ANCOVA Parametric	Mean	$F = \frac{MS_{adj}}{MSE_{adj}}$	Normality Independence Equal Variance Linearity of Covariate Homogeneity of Regression
Dunnett's Test Parametric	Mean	$d = \frac{\bar{x}_i - \bar{x}_c}{\sqrt{2MSE/n}}$	Normality Independence Equal Variance
Friedman Test Non-parametric	Mean	$\chi^2(df) = \frac{12}{bk(k+1)}\sum R_j^2 - 3b(k+1)$	Dependent Groups Ordinal Data
Games-Howell Test Parametric	Mean	$q = \frac{\bar{x}_i - \bar{x}_j}{\sqrt{\frac{s_i^2}{n_i} + \frac{s_j^2}{n_j}}}$	Normality Independence Equal Variance
Kruskal-Wallis Test Non-parametric	Mean	$H = \frac{12}{N(N+1)}\sum_{i=1}^k \frac{R_i^2}{n_i} - 3(N+1)$	Independence Ordinal Data
MANOVA Parametric	Mean	$\Lambda = \frac{\|\mathbf{E}\|}{\|\mathbf{H} + \mathbf{E}\|}$	Normality Independence Equal Variance
One Sample Z-Test Parametric	Mean	$z = \frac{\bar{x} - \mu_0}{\sigma/\sqrt{n}}$	Normality Independence Known σ
One Sample t-Test Parametric	Mean	$t = \frac{\bar{x} - \mu_0}{s/\sqrt{n}}$	Normality Independence Known σ
One Way ANOVA Parametric	Mean	$F = \frac{MSG}{MSE}$	Normality Independence Equal Variance
Paired t-Test Parametric	Mean	$t = \frac{\bar{d}}{s_d/\sqrt{n}}$	Normality Paired Data
Permutation Test Non-parametric	Mean	$p = \frac{\sum_{i=1}^{N} I(T^*_i \geq T_{obs})}{N}$	Independence Normality Equal Variance
Repeated Measures ANOVA Parametric	Mean	$F = \frac{MSG}{MSE}$	Normality Dependent Groups Sphericity
Scheffé Test Parametric	Mean	$F_s = \frac{(\bar{x}_i - \bar{x}_j)^2}{MSE(\frac{1}{n_i} + \frac{1}{n_j})}$	Normality Independence Equal Variance
Three-way ANOVA Parametric	Mean	$F = \frac{MSG}{MSE}$	Normality Independence Equal Variance
Tukey's HSD Test Parametric	Mean	$q = \frac{\bar{x}_i - \bar{x}_j}{\sqrt{MSE/n}}$	Normality Independence Equal Variance
Two Sample Z-Test Parametric	Mean	$Z = \frac{(\bar{x}_1 - \bar{x}_2) - (\mu_1 - \mu_2)}{\sqrt{\frac{\sigma_1^2}{n_1} + \frac{\sigma_2^2}{n_2}}}$	Normality Independence Known σ
Two Sample t-Test (Pooled variance) Parametric	Mean	$t = \frac{\bar{x}_1 - \bar{x}_2}{s_p\sqrt{\frac{1}{n_1} + \frac{1}{n_2}}}$	Normality Independence Known σ Equal Variance
Two Sample t-Test (Welch's) Parametric	Mean	$t = \frac{\bar{x}_1 - \bar{x}_2}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}}$	Normality Independence Known σ Equal Variance
Two Way ANOVA Parametric	Mean	$F = \frac{MSG}{MSE}$	Normality Independence Equal Variance
Welch's ANOVA Parametric	Mean	$W = \frac{\sum w_i(\bar{x}_i - \hat{\mu})^2 / (k-1)}{1 + \frac{2(k-2)}{k^2-1}\sum \frac{(1-w_i/\sum w_j)^2}{n_i - 1}}$	Normality Independence Equal Variance
Chi-Square Goodness of Fit Test Parametric	Proportion	$\chi^2 = \sum \frac{(O - E)^2}{E}$	Normality Independence
Chi-Square Test of Independence Parametric	Proportion	$\chi^2 = \sum \frac{(O - E)^2}{E}$	Normality Independence
Fisher's Exact Test Non-parametric	Proportion	$p = \frac{(a+b)!(c+d)!(a+c)!(b+d)!}{n!a!b!c!d!}$	Independence Fixed Row/Column Totals
One Sample Proportion Test Parametric	Proportion	$z = \frac{\hat{p} - p_0}{\sqrt{\frac{p_0(1-p_0)}{n}}}$	Independence Binomial Data
Two Sample Proportion Test Parametric	Proportion	$Z = \frac{\hat{p}_1 - \hat{p}_2}{\sqrt{\hat{p}(1-\hat{p})(\frac{1}{n_1} + \frac{1}{n_2})}}$	Independence Binomial Data
Dunn's Test Non-parametric	Rank	$z_{ij} = \frac{\bar{R}_i - \bar{R}_j}{\sqrt{\frac{N(N+1)}{12}(\frac{1}{n_i} + \frac{1}{n_j})}}$	Independence Ordinal Data
Mann-Whitney U Test Non-parametric	Rank	$Z = \frac{U - \mu_U}{\sigma_U}$	Independence Ordinal Data
Wilcoxon Signed Rank Test Non-parametric	Rank	$Z = \frac{W - \mu_W}{\sigma_W}$	Paired Data Ordinal Data

Descriptive Statistics Calculators

Choose from our comprehensive collection of descriptive statistics calculators for both quantitative and qualitative data analysis.

Central Tendency

Calculator	Formula	Description
Mean	$\bar{x} = \frac{\sum_{i=1}^n x_i}{n}$	Calculate arithmetic average of numerical data
Median	$\text{Middle value when ordered}$	Find the middle value in ordered data
Mode	$\text{Most frequent value}$	Identify most common value(s) in dataset
Geometric Mean	$\sqrt[n]{x_1 \times x_2 \times ... \times x_n}$	Calculate mean for multiplicative relationships
Harmonic Mean	$\frac{n}{\sum_{i=1}^n \frac{1}{x_i}}$	Calculate mean for rates and speeds

Variability

Calculator	Formula	Description
Standard Deviation	$s = \sqrt{\frac{\sum(x_i - \bar{x})^2}{n-1}}$	Measure average deviation from mean
Mean Absolute Deviation	$\text{MAD} = \frac{\sum \|x_i - \bar{x}\|}{n}$	Measure average deviation from mean
Variance	$s^2 = \frac{\sum(x_i - \bar{x})^2}{n-1}$	Measure spread of data points
Range	$\text{max}(x) - \text{min}(x)$	Calculate difference between largest and smallest values
IQR	$Q_3 - Q_1$	Calculate spread of middle 50% of data
Coefficient of Variation	$CV = \frac{s}{\bar{x}} \times 100\%$	Compare variability between datasets

Position

Calculator	Formula	Description
Percentiles	$P_k = \text{value at }k\text{th percentile}$	Find value at specified percentile
Z-Score	$z = \frac{x - \bar{x}}{s}$	Calculate standardized scores

Shape

Calculator	Formula	Description
Skewness	$\frac{\sum(x_i - \bar{x})^3}{(n-1)s^3}$	Measure asymmetry of distribution
Kurtosis	$\frac{\sum(x_i - \bar{x})^4}{(n-1)s^4}$	Measure tailedness of distribution

Relationships

Calculator	Formula	Description
Correlation Coefficient	$r = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum (x_i - \bar{x})^2 \sum (y_i - \bar{y})^2}}$	Measure linear relationship strength with Pearson's r

Probability Distribution Calculators

Access our suite of probability distribution calculators for both discrete and continuous random variables. Calculate probabilities, find critical values, and visualize distributions.

Common Discrete

Distribution	Probability Function	Description
Binomial	$P(X=k) = \binom{n}{k}p^k(1-p)^{n-k}$	Model number of successes in fixed trials
Poisson	$P(X=k) = \frac{\lambda^k e^{-\lambda}}{k!}$	Model rare events in fixed interval
Geometric	$P(X=k) = p(1-p)^{k-1}$	Model trials until first success
Negative Binomial	$P(X=k) = \binom{k-1}{r-1}p^r(1-p)^{k-r}$	Model trials until r successes
Hypergeometric	$P(X=k) = \frac{\binom{K}{k}\binom{N-K}{n-k}}{\binom{N}{n}}$	Model sampling without replacement

Chart Makers

Choose the right visualization based on your data type and analysis goals. Our tools help you create clear, effective visual representations of your data.

Categorical Data

Chart Type	Description	Best Used For	Example
Bar Chart	Display frequencies or counts for categories	Comparing categories, showing distributions	Product sales by category
Pie Chart	Show part-to-whole relationships	Displaying proportions, percentages	Market share by company
Funnel Chart	Visualize conversion rates and stages in a process	Process flows, conversion funnels	Sales funnel conversion rates
Tree Map	Display hierarchical data using nested rectangles	Hierarchical composition, proportional sizes	Market share by product category
Waterfall Chart	Visualize cumulative values, gains and losses	Financial analysis, breakdown visualization	Profit and loss breakdown

Numerical Data

Chart Type	Description	Best Used For	Example
Histogram	Display distribution of continuous data	Examining data distribution shape	Age distribution of customers
Box Plot	Show data distribution and outliers	Identifying outliers, comparing distributions	Test scores distribution
Violin Plot	Combine box plot with kernel density	Detailed view of data distribution	Income distribution by department
Dot Plot	Visualize individual data points and their distribution	Small datasets, simple distributions	Daily temperature readings
Stem-and-Leaf Plot	Show distribution while preserving individual values	Small to medium datasets, retaining data values	Test scores with individual values
Density Plot	Visualize data distribution using kernel density estimation	Smooth distribution visualization	Income distribution analysis
Bell Curve Graph Generator	Visualize normal distribution with mean and standard deviation	Normal distribution visualization	IQ score distribution
Q-Q Plot	Assess normality and compare data distributions	Testing normality assumptions	Checking if data follows normal distribution

Confidence Interval Calculators

Choose from our comprehensive collection of confidence interval calculators for estimating population parameters and analyzing differences between groups.

Population Parameters

Interval Type	Description	Best Used For	Example
Mean	Estimate population mean	Continuous data, normal distribution	Average customer spending ± margin of error
Proportion	Estimate population proportion	Binary outcomes, categorical data	Customer satisfaction rate ± margin of error
Standard Deviation	Estimate population variability	Process variation, quality control	Manufacturing tolerance limits

Regression Analysis Calculators

Choose from our collection of regression analysis tools for modeling relationships between variables and making predictions from your data.

Linear Regression

Model Name	Description	Example
Simple Linear Regression	Model relationship between two continuous variables	Height vs weight relationship
Multiple Linear Regression	Model with multiple predictors	House price prediction using area, location, age

Non-Linear Regression

Model Name	Description	Example
Quadratic Regression	Model curved relationships with quadratic terms	Projectile motion, optimal pricing models
Exponential Regression	Model exponential growth or decay patterns	Population growth, radioactive decay, compound interest

Classification Models

Model Name	Description	Example
Logistic Regression	Model binary outcomes	Customer churn prediction

Limited Dependent Variable Models

Model Name	Description	Example
Censored Regression (Tobit)	Fit Tobit models for censored continuous outcomes	Hours worked when many observations are zero

Multivariate Analysis Calculators

Analyze multiple variables simultaneously with our multivariate tools — reduce dimensions, classify groups, and quantify relationships across variable sets.

Dimensionality Reduction

Method	Description	Example
Principal Component Analysis (PCA)	Reduce dimensionality by finding directions of maximum variance	Compress 50 survey items into 3 latent dimensions
Exploratory Factor Analysis	Identify latent factors that explain observed correlations	Discover underlying personality traits from items
Confirmatory Factor Analysis	Test a hypothesized factor structure against your data	Confirm a 3-factor structure for a published instrument

Classification

Method	Description	Example
Discriminant Analysis (LDA/QDA)	Classify observations and project to maximize group separation	Predict species from morphological measurements

Relationships

Method	Description	Example
Canonical Correlation Analysis	Examine relationships between two sets of variables	Relate test battery to job-performance metrics

Survival Analysis Calculators

Analyze time-to-event data — model survival curves, compare groups, and quantify covariate effects on hazard rates with APA-formatted output.

Time-to-Event Analysis

Method	Description	Example
Kaplan-Meier Survival Analysis	Estimate survival probabilities over time with censoring	Patient survival after treatment over 5 years
Log-Rank Test	Compare survival curves between two or more groups	New drug vs. standard treatment survival
Cox Proportional Hazards Regression	Model hazard rates with covariates and produce hazard ratios	Effect of age and stage on cancer survival

Statistical Resources

Access our collection of statistical tables, interactive simulations, and reference materials to support your statistical analysis.

Distribution Tables

Resource	Description
Z-Table	Standard normal distribution critical values for Z-tests and standardized scores
T-Table	Student's t-distribution critical values, ideal for small sample tests with unknown population σ
Chi-Square Table	Chi-square distribution critical values used in goodness of fit and independence tests
F-Table	F-distribution critical values for ANOVA and variance comparisons
Wilcoxon Signed-Rank Table	Critical values for Wilcoxon signed-rank test and paired sample comparisons

Analyze Your Data with Confidence

Fast, accurate statistical analysis trusted by students and researchers