AI-Assisted Data Analysis

From Questions to Code to Results

🔄 Quick Recall: In the previous lesson, you used AI to identify research gaps, generate hypotheses, and design studies. Now you’ll use AI to analyze the data those studies produce — whether you write code or prefer conversation-based tools.

Data analysis is where many researchers hit a bottleneck. You know what question you’re asking, you know what test you need, but the code takes hours to debug. AI eliminates that bottleneck — if you know how to use it correctly.

Two Paths: Code-Based and No-Code

Code-based (Python/R): AI writes, debugs, and explains analysis code.

No-code (Julius, Databot, ChatGPT): Upload data, describe your analysis in plain English, get results.

Both paths require the same thing from you: understanding whether the analysis is appropriate for your data. AI writes the code; you evaluate the logic.

AI-Assisted Coding: Python and R

For researchers who code (or want to start), AI transforms the workflow:

I have a dataset with these variables:
- [Variable 1]: [type, range, description]
- [Variable 2]: [type, range, description]
- [Outcome variable]: [type, range, description]

Research question: [your question]
Study design: [experimental, observational, etc.]
Sample size: [N]

Write [Python/R] code to:
1. Load and clean the data (handle missing values, check data types)
2. Generate descriptive statistics and check distributions
3. Test assumptions for [planned statistical test]
4. Run the analysis
5. Create a publication-quality figure of the results
6. Report results in APA format

✅ Quick Check: Why include study design and variable types in the prompt? Because the same research question requires different statistical tests depending on these factors. “Does treatment affect outcome?” needs an independent t-test for two groups, ANOVA for three or more, mixed-effects model for repeated measures. AI needs your study details to choose correctly.

Choosing Between Python and R

The practical answer: Use what your field uses and your collaborators can read. AI can write in either language equally well.

The Verification Protocol

AI-generated code requires systematic verification before you trust the results:

Step 1: Logic check

• Does the code use the correct statistical test for your data type and design?
• Are variables correctly specified (dependent vs. independent, fixed vs. random)?
• Are categorical variables coded correctly?

Step 2: Assumption check

• Does the code test assumptions (normality, homoscedasticity, independence)?
• If assumptions are violated, does it use appropriate alternatives?

Step 3: Output check

• Do descriptive statistics match what you expect from your data?
• Are sample sizes correct (no silent data drops)?
• Do results make theoretical sense?

Review this [Python/R] analysis code for my study:

[paste your code]

Check for:
1. Is this the correct statistical test for [my study design]?
2. Are assumptions properly tested?
3. Are there any errors in variable specification?
4. Could any data handling steps silently drop observations?
5. Are the results reported correctly?

No-Code Analysis Tools

For researchers who don’t code — or who want quick exploratory analysis:

No-code workflow:

1. Upload your dataset (CSV, Excel)
2. Ask: “Describe this dataset — what variables do I have, what are the distributions, are there missing values?”
3. Ask: “I want to test whether [X] affects [Y]. What analysis should I use given this data?”
4. Ask: “Run that analysis and show me the results”
5. Ask: “Create a figure showing these results”

✅ Quick Check: Do no-code tools eliminate the need to understand statistics? No. They eliminate the need to write code, not the need to evaluate whether the analysis is appropriate. If Julius runs a t-test on your non-normal data with unequal variances, you need to know that a Welch’s t-test or Mann-Whitney U would be more appropriate. The tool runs whatever you ask — your statistical knowledge determines whether what you asked for was correct.

Publication-Quality Visualizations

AI generates quick visualizations. Turning them into publication figures requires specific adjustments:

Create a publication-quality figure for [journal name]:
- Dimensions: [width] x [height] inches
- Resolution: 300 DPI minimum
- Font: Arial/Helvetica, [size] pt for labels
- Color palette: colorblind-accessible (viridis or similar)
- Include: error bars (95% CI), significance markers where p < 0.05
- Format: PDF (vector) for submission, PNG for review
- Style: consistent with [journal] figure guidelines

Common figure problems AI can fix:

• Overlapping labels → adjust spacing and rotation
• Unreadable at print size → increase font sizes, simplify
• Not colorblind-accessible → switch to viridis, cividis, or sequential palettes
• Low resolution → re-export at 300+ DPI
• Inconsistent styling → create a style template for all figures

Interpreting Results with AI

After analysis, AI helps interpret and contextualize:

Here are my analysis results:
[paste statistical output]

Help me interpret:
1. What do these results mean in plain language?
2. What's the effect size, and is it practically significant (not just statistically)?
3. What are the limitations of this analysis?
4. How do these results compare to typical findings in [field]?
5. What follow-up analyses might strengthen these findings?

Critical distinction: AI interprets the numbers. You interpret the meaning. A statistically significant result with a tiny effect size might not matter. A non-significant result in an underpowered study doesn’t mean no effect exists. These judgments require your domain expertise.

Key Takeaways

• AI writes analysis code in Python or R — but you must verify the statistical logic, not just whether the code runs
• Choose Python or R based on your field’s conventions and your analysis needs, not AI’s default
• No-code tools (Julius, Databot) eliminate the coding barrier but still require statistical understanding
• Publication-quality figures need specific formatting: 300+ DPI, colorblind-accessible palettes, journal-compliant dimensions
• AI interprets numbers; you interpret meaning — effect sizes, practical significance, and theoretical implications require your expertise

Up Next: You’ll learn to use AI for scientific writing — drafting manuscript sections while maintaining your scholarly voice and meeting journal standards.