Statistical analysis

Sourcetable provides access to the full SciPy and StatsModels libraries for rigorous statistical analysis. Describe what you need in plain English and the AI writes and executes the correct statistical test.

Hypothesis testing

t-tests

"Run an independent t-test comparing conversion rates between group A and group B"

Supported t-test variants:

Test	Use case
Independent samples t-test	Compare means between two groups
Paired samples t-test	Compare before/after measurements
One-sample t-test	Compare a sample mean to a known value
Welch’s t-test	Compare groups with unequal variances

Chi-square tests

"Run a chi-square test to see if product category and customer segment are independent"

Chi-square test of independence — test association between categorical variables
Chi-square goodness of fit — test if data follows an expected distribution

ANOVA

"Run a one-way ANOVA comparing customer satisfaction scores across all 5 regions"

One-way ANOVA — compare means across 3+ groups
Two-way ANOVA — test two factors and their interaction
Tukey’s HSD — post-hoc pairwise comparisons after significant ANOVA

Non-parametric tests

Test	Use case
Mann-Whitney U	Compare two independent groups (non-normal data)
Wilcoxon signed-rank	Compare paired observations (non-normal data)
Kruskal-Wallis	Compare 3+ groups (non-normal data)
Kolmogorov-Smirnov	Test if data follows a specific distribution
Shapiro-Wilk	Test for normality

Regression analysis

Linear regression

"Build a linear regression model predicting house price from square footage, bedrooms, and age"

Output includes: coefficients, R-squared, adjusted R-squared, p-values, confidence intervals, residual plots, and VIF for multicollinearity.

Logistic regression

"Build a logistic regression to predict customer churn from usage metrics"

Output includes: odds ratios, ROC curve, AUC, confusion matrix, classification report.

Multiple regression

"Run a stepwise regression to find the best predictors of employee retention"

Supports forward selection, backward elimination, and stepwise methods with AIC/BIC criteria.

Correlation

Method	When to use
Pearson	Linear relationship between continuous variables
Spearman	Monotonic relationship, ordinal data, or non-normal distributions
Kendall	Small samples or many tied ranks
Point-biserial	Correlation between binary and continuous variables

Effect size measures

The AI automatically reports effect sizes alongside p-values:

Cohen’s d — for t-tests (small: 0.2, medium: 0.5, large: 0.8)
Eta-squared — for ANOVA (proportion of variance explained)
Cramér’s V — for chi-square tests (association strength)
R-squared — for regression (variance explained)

Distribution fitting

"Fit the best probability distribution to the response time data"

Tests against normal, log-normal, exponential, gamma, Weibull, and beta distributions. Reports goodness-of-fit statistics (KS test, AIC) for each.

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Hypothesis testing

t-tests

Chi-square tests

ANOVA

Non-parametric tests

Regression analysis

Linear regression

Logistic regression

Multiple regression

Correlation

Effect size measures

Distribution fitting

​Hypothesis testing

​t-tests

​Chi-square tests

​ANOVA

​Non-parametric tests

​Regression analysis

​Linear regression

​Logistic regression

​Multiple regression

​Correlation

​Effect size measures

​Distribution fitting

Hypothesis testing

t-tests

Chi-square tests

ANOVA

Non-parametric tests

Regression analysis

Linear regression

Logistic regression

Multiple regression

Correlation

Effect size measures

Distribution fitting