Symbiotic Scholar Suite
Upload your dataset. Select your analysis. Get APA 7th edition formatted tables, automatic assumption checks, and an AI-drafted Results paragraph — in under 60 seconds. No SPSS required.
Free tier includes 3 analyses. No credit card required.
Upload Dataset
📄 spiderphobia_study.csv
20 rows · 3 columns · Parsed in 0.1s
Variables Detected
12
Statistical tests
Parametric & non-parametric
< 60 sec
Average time to results
From upload to APA output
APA 7
Edition compliance
Tables, notation & narratives
SPSS-equivalent
Statistical accuracy
Verified against reference datasets
Process
Three steps from your data file to APA-formatted, committee-ready results.
Drag in a CSV, Excel, or SPSS .sav file. QuantAI auto-detects variable types and survey platform exports.
Choose your statistical test and map your variables. Assumption checks run automatically before computation.
Independent Samples t-Test
Compare means between two independent groups
Dependent variable
anxiety_score continuousGrouping variable
group binaryAPA 7 table, assumption flags, and an AI-drafted Results paragraph — ready to paste into Chapter 4.
Note. t(241.8) = −7.39, p < .001, d = 0.98.
"An independent samples t-test revealed a statistically significant difference…"
See It In Action
Upload a dataset, pick a test, and get an APA-formatted table with an AI-drafted Results paragraph — in under 60 seconds. No SPSS license. No syntax. No waiting.
New Analysis
Step 1 of 3dissertation_data.csv
342 rows · 8 columns detected
Select Analysis
Step 2 of 3Independent t-Test
Compare means between two groups
One-Way ANOVA
Compare three or more groups
Pearson Correlation
Measure linear relationship
Assign Variables
Independent t-Test
completeTable 1
Independent Samples t-Test Results
| Group | n | M | SD |
|---|---|---|---|
| Control | 171 | 52.41 | 14.22 |
| Treatment | 171 | 62.63 | 13.85 |
Note. t(340) = −5.241, p < .001, Cohen's d = 0.73.
APA 7 Results Paragraph
An independent samples t-test was conducted to compare exam scores between the control group (M = 52.41, SD = 14.22) and the treatment group (M = 62.63, SD = 13.85). The results indicated a statistically significant difference, t(340) = −5.24, p < .001, Cohen's d = 0.73…
Results also export as a formatted .docx — paste directly into your manuscript.
Statistical Intelligence
QuantAI doesn't just run tests — it actively checks your data quality, flags methodological concerns, and produces output your committee can scrutinize.
Assumption Checking
Before computing a single statistic, QuantAI runs a full assumption check suite. Every flag includes a plain-language explanation and, where appropriate, a recommendation — so you know exactly what to report in your Methods section.
Assumption Report · One-Way ANOVA
Levene's test indicates unequal variances. Welch's correction has been applied automatically. Report this in your Methods section.
APA Table Output
Table 1
Mann-Whitney U Test Results
| Group | n | Mdn | IQR |
|---|---|---|---|
| Drug A | 10 | 4.50 | 2.50 |
| Drug B | 10 | 8.00 | 2.00 |
Note. U = 14.0, p = .007, |r| = 0.720 (effect size).
APA Table Output
Every table follows APA 7th edition formatting exactly: no vertical lines, horizontal rules at the header and bottom, proper notation for all statistics, and a table note reporting effect sizes and key values. Paste directly into your manuscript.
Effect sizes are always included — Cohen's d, η², rank-biserial r, Cramér's V, or R² — because a p-value alone does not satisfy APA requirements.
AI Results Narrative
Claude reads your statistical output and writes an 80–150 word Results paragraph in APA 7th edition style — correct notation, proper effect size interpretation, and language appropriate for a peer-reviewed manuscript. Treat it as a well-informed first draft: review, edit, and take ownership before submitting.
Correct statistical notation (t, F, p, M, SD italicized)
Effect size interpretation included
Assumption violation notes where applicable
Language calibrated for academic manuscripts
Sample AI Narrative · Mann-Whitney U
"A Mann-Whitney U test was conducted to examine differences in pain scores between participants receiving Drug A (Mdn = 4.50, IQR = 2.50) and Drug B (Mdn = 8.00, IQR = 2.00). The test revealed a statistically significant difference between groups, U = 14.0, p = .007, indicating that participants receiving Drug A reported substantially lower pain scores. The rank-biserial correlation |r| = 0.720 suggests a large effect size (Cohen, 1988)."
12 Analyses
Not just a checklist — each analysis includes automatic assumption checks, APA-formatted output, and an AI-drafted Results paragraph.
When to use: Compare two independent groups on a continuous outcome
t, df, p, Cohen's d, APA table
When to use: Compare the same group measured twice (pre/post)
t, df, p, Cohen's d, APA table
When to use: Compare three or more independent groups
F, df, p, η², Tukey post-hoc, APA table
When to use: Compare two groups when normality is violated
U, p, rank-biserial r, APA table
When to use: Compare three+ groups without normality assumption
H, df, p, η², APA table
When to use: Paired comparison when normality is violated
W, p, effect size r, APA table
When to use: Measure linear relationship between two continuous variables
r, p, 95% CI, APA table
When to use: Measure monotonic relationship for ordinal or non-normal data
ρ, p, APA table
When to use: Predict a continuous outcome from one or more predictors
R², F, β, SE, p per predictor, APA table
When to use: Predict a binary outcome (yes/no, pass/fail)
OR, 95% CI, p, model fit, APA table
When to use: Test association between two categorical variables
χ², df, p, Cramér's V, APA table
When to use: Assess internal consistency of a multi-item scale
α, 95% CI, item-total correlations, APA table
Verified Accuracy
We ran the same datasets through QuantAI, SPSS, and R independently. Every statistic matches to reported decimal precision — because they all use the same validated mathematical formulas.
Independent t-test
Spider Phobia (n = 20) · t(18) = −7.385 · p < .001
−7.385
Match−7.385
Match−7.385
MatchReference dataset available — run it yourself to verify.
Download datasetAll 12 test types independently verified
Reference datasets are sourced from published statistics textbooks with known expected values. Download any dataset, run it in SPSS or R, and compare — the numbers will match.
Who It's For
Chapter 4 of your dissertation requires APA-formatted tables, effect sizes, and a written Results section. QuantAI produces all three in under 60 seconds — committee-ready, every time.
Run rapid analyses for grant proposals, conference submissions, and peer-reviewed manuscripts. Get publication-ready output without toggling between SPSS, APA style guides, and Word.
Support multiple researchers with a shared Doctoral Pro account. Each user gets unlimited analyses, SPSS upload, and full .docx export across all 12 statistical tests.
Researcher Feedback
What researchers say after their first analyses.
“I ran my entire Chapter 4 — three t-tests, an ANOVA, and a correlation matrix — in one afternoon. The APA tables came out exactly as my committee expects them. I didn't touch SPSS once.”
Doctoral Candidate
Educational Psychology · PhD
“The assumption checking is what sets this apart. Every time I've used SPSS I had to remember to run Levene's and Shapiro-Wilk separately. Here it just happens, with a plain-English explanation I can paste directly into my Methods section.”
Dissertation Student
Organizational Leadership · EdD
“I was skeptical about AI-generated statistics, but the methodology page answered every concern. The numbers match my R output to three decimal places. I now use QuantAI for first-pass analyses and verify the key ones in R when I need to — saves hours.”
Academic Researcher
Social Sciences · Post-doc
Honest Answers
AI-assisted statistical analysis is new. Skepticism is healthy. Here are the questions most researchers ask before their first analysis — and straight answers.
QuantAI does not generate statistics — it runs them. The computations use scipy.stats and statsmodels, the same validated mathematical libraries that power R and academic Python research. The AI writes only the narrative paragraph; every number comes from a deterministic statistical function. You can verify any result against SPSS or R using our published reference datasets.
Yes — provided you report your methods accurately. State that analyses were conducted using QuantAI (which uses scipy.stats for computation) and verified against APA 7th edition reporting standards. The output follows APA style as required by virtually all social science journals and dissertation committees. Many researchers use QuantAI for speed and then independently verify key results.
For the statistical output, the results match to reported decimal precision — same formulas, same underlying math. The difference is workflow: QuantAI adds automatic assumption checking before every analysis, an AI-drafted Results paragraph in APA style, and a .docx export ready to paste into your manuscript. SPSS costs $1,200–$3,000+/year depending on edition and add-ons. QuantAI starts at $12/month.
Your uploaded data is stored in an encrypted Supabase database accessible only to your account. It is used solely to run your analyses. Your raw data is never sent to any AI model — only the statistical results and variable names are passed to Claude to generate the narrative paragraph. You can delete your datasets at any time from your dashboard.
The statistical computation is identical to running SPSS or R — there is nothing methodologically novel about the analysis itself. What QuantAI adds is an AI-drafted Results paragraph, which you should review, edit, and take ownership of before submission (as you would any writing assistance). Disclose your analytical tools as you normally would in your Methods section.
"QuantAI was designed for researchers who need to defend every methodological decision to a dissertation committee, a peer reviewer, and an IRB — all at once. Every design decision reflects that standard."
— QuantAI Design Principles
Pricing
Start free — no credit card required. Upgrade when you need more analyses or .docx export.
Explore QuantAI with 3 complete analyses.
All plans include end-to-end encryption and full assumption reporting. Cancel anytime. No contracts.
FAQ
Yes. QuantAI uses scipy.stats and statsmodels — the same validated libraries used in academic Python research — to compute all statistics. Results match R and SPSS to the same decimal precision for the same data. We publish reference datasets with verified expected values so you can confirm this yourself.
All effect sizes follow APA 7th edition conventions: Cohen's d for t-tests, η² for ANOVA and Kruskal-Wallis, rank-biserial correlation r for Mann-Whitney and Wilcoxon, Cramér's V for chi-square, and R² for regression. Effect sizes are reported alongside p-values in every analysis.
Before every analysis: missing data percentage and recommendation, normality (Shapiro-Wilk for n<50, D'Agostino-Pearson for n≥50), homogeneity of variance (Levene's test for group comparisons), sample size adequacy, and multicollinearity (VIF) for regression. Each check returns a green/yellow/red flag with a plain-language explanation.
QuantAI flags the violation and recommends a non-parametric alternative — but still runs the analysis you requested. You decide how to proceed. The assumption flags are advisory, not blocking.
Every table follows APA 7th edition formatting: no vertical lines, horizontal rules at top/bottom and below the header, proper column spacing, and a table note reporting key statistics. The AI narrative uses correct statistical notation (italicized test statistics, exact p-values, effect sizes) as required by APA style.
Yes — Scholar Pass and Doctoral Pro include .docx export on every analysis. The Word document contains the APA-formatted table and the AI-drafted Results paragraph. It is formatted to be pasted directly into a manuscript with minimal editing.
Treat it like a well-informed first draft. It accurately reports all statistics in APA style, but you should review it for accuracy, adjust to your specific research context, and take ownership of the writing before submission.
Free tier: CSV. Scholar Pass and Doctoral Pro: CSV, Excel (.xlsx), and SPSS (.sav) files. Variable types (continuous, binary, ordinal, categorical) are detected automatically on upload.
No. Your raw data never leaves QuantAI's servers. Only the statistical results (numbers, variable names, test type) are sent to Claude to generate the narrative paragraph. Claude never sees your respondent-level data.
Yes. You can delete any dataset from your dashboard at any time. Deletion removes the file from storage and all associated analysis records from the database.
3 analyses total (lifetime), CSV upload only, no .docx export, all 12 statistical tests. No credit card required.
For Scholar Pass: yes, 50 analyses per month. For the free tier: 3 analyses total (lifetime, not monthly). For Doctoral Pro: unlimited — no counting.
Yes. Cancel from your account page at any time. Your subscription remains active until the end of the billing period. No cancellation fees.
Have a question not answered here? Contact us — we respond to all research inquiries within 24 hours.
🔒 Data Privacy
QuantAI uses Anthropic's enterprise API — not the consumer product. By policy, API inputs are never retained or used to train AI models. Your research data lives only in your secure, private account.