The AI kit for Statisticians

Picks the right test, checks assumptions, and writes the APA 7 result, for example a paired t-test with effect size and CI.

Builds publication-quality statistical graphics in Python, like a faceted boxplot of treatment groups with confidence intervals.

Sizes and reads A/B tests, for example telling you whether a 2% lift is real at your traffic level.

Guides missing values, outliers, duplicates, and transformations, for example imputing lab values with a documented rule.

Like "Translate Request Into Testable Hypotheses"

Like "Generate Effective Survey Questions"

Like "Write A Reproducible Data Cleaning Script"

Like "Choose The Right Statistical Test"

Experiment samples that are valid before anyone draws conclusions.

A reproducible figure bundle whose values and labels match the analysis.

Every analysis has its assumptions checked, with violations decided by the investigator.

A single-change test plan that is launched correctly and read at the right time

Let your AI write and run code inside your Jupyter Notebook, cell by cell, and read the results.

Lets your AI run R code in a live session, keep results between steps, and make plots for you.

Lets your AI look at your BigQuery tables and run SQL queries for you.

Let your AI open, edit and run Jupyter notebooks for you while you watch.

DataLab is a data notebook with generative AI that lets you interact naturally with your data, edit and run analyses in a powerful IDE, and create polished, sha

Sliq is an AI data-cleaning tool that detects field context to fix dates, typos, and missing values automatically, cutting 70-80% of tedious cleaning so teams c

DataMotto automates data preprocessing by cleansing, enriching, and preparing your raw data, reducing manual effort and saving you hours in making data ready fo

Einblick Prompt AI for JupyterLab transforms your data notebooks by generating and correcting code, creating charts, and building models from simple prompts usi

Keeps sources, notes, and drafts in one place so your citations stay correct while you write.

Cuts the time from research question to a cited, reviewable report you own.

Applies reversible, documented cleaning rules you approve, so your dataset stays auditable.

Reduce implementation drift from an approved plan.

Runs statistical tests and reports results in APA format for academic research.

Fits and diagnoses statistical models for rigorous inference and forecasting.

Plan statistically valid A/B tests with locked hypothesis, sample size, and pre-launch checklist.

Finds what is actually wrong in a messy spreadsheet before anyone builds a chart on it.