Statistical Analysis and Interpretation with AI

Statistics is where data analyst work goes wrong most often. Not because analysts do not know the math, but because stakeholders do not know the math — so a correct result can be explained incorrectly, a significant effect can be oversold, and a non-significant result can be misreported as "no difference."

AI helps in two places: running the right test, and explaining the result in plain language. This lesson covers both.

What You'll Learn

• Picking the right statistical test with AI as a reference
• Running and interpreting A/B tests, t-tests, chi-squared, and regressions
• Translating statistical jargon into stakeholder-ready language
• Spotting statistical mistakes AI might make, so you do not repeat them

Picking the Right Test

If you are unsure whether to use a t-test, Mann-Whitney, chi-squared, or regression, AI is an excellent reference. Template:

I want to compare two groups:

• Group A: conversion rate 3.2%, n = 4,210
• Group B: conversion rate 3.7%, n = 4,198

Both groups come from a randomized A/B test on our checkout flow. Each user appears in exactly one group. The metric is binary (converted / did not convert).

Which statistical test should I run? Justify your choice in two sentences and name the assumptions it requires. Then run the test (pseudocode in Python with scipy) and return the p-value, the 95% confidence interval for the lift, and a plain-language interpretation.

The AI will choose a two-proportion z-test (or chi-squared), not a t-test, because the outcome is binary. It will also give you the CI, which is more useful to stakeholders than the p-value alone.

Running A/B Tests

A/B tests are the most common analyst statistics task. Template:

I ran an A/B test for 14 days.

• Variant A (control): 50,211 users, 1,602 conversions

• Variant B (treatment): 50,144 users, 1,781 conversions

1. Compute the conversion rate for each group
2. Compute the relative lift (B/A − 1)
3. Run a two-proportion z-test for significance
4. Compute the 95% confidence interval for the absolute and relative lift
5. Report the minimum detectable effect given the sample size and 80% power
6. Translate all of this into a 3-sentence summary for a non-technical PM

The AI will return numbers you can verify by running the code yourself. The 3-sentence summary is what you paste into Slack or the launch doc.

Watch for these A/B test traps

AI can get these wrong if you do not prompt carefully:

• Peeking. Running the test halfway through and stopping when it looks significant inflates false positives. Prompt: "Assume we committed to 14 days before the test started and did not peek."
• Multiple metrics. If you check 10 metrics at p < 0.05, one will look significant by chance. Prompt: "Apply a Bonferroni correction for 10 metrics."
• Non-independent observations. If one user appears multiple times, a standard test is wrong. Prompt: "Each user appears in exactly one group exactly once."
• Sample size plans. Do not wait until after the test to decide sample size. Ask AI to compute it beforehand: "What sample size do I need to detect a 5% relative lift from a 3.2% baseline at 80% power, two-sided test, alpha = 0.05?"

T-tests and Their Relatives

When comparing two continuous numbers (e.g., average order value):

I want to compare average order value between two customer segments.

• Segment A: AOV mean = $48.10, SD = $72.40, n = 3,211
• Segment B: AOV mean = $54.90, SD = $81.80, n = 2,844

AOV is right-skewed with a long tail.

1. Should I use a Welch's t-test, a Mann-Whitney U test, or log-transform first?
2. Run the test I should use and report the result
3. Report the 95% CI for the difference in means (bootstrap if distribution is not normal)
4. Write a one-paragraph plain-language interpretation
5. State one practical limitation of this analysis

AI will usually recommend either log-transforming or running a Mann-Whitney (non-parametric) given the skew.

Chi-squared for Categorical Data

If you want to know whether product choice depends on country:

I have a cross-tab of product purchases by country. Chi-squared the table and report:

• The test statistic and p-value
• Expected vs observed counts for the three largest cells
• Cramer's V as an effect size
• Which specific country × product combinations are over- or under-represented

[Paste cross-tab.]

The "which cells" part is the useful part — stakeholders care about where the deviation is, not just that a deviation exists.

Regression and Its Interpretation

Regression is where analysts often undersell the result.

I ran a linear regression of monthly_spend (dependent) on tenure_months, num_products, and segment (categorical: small, mid, enterprise).

Here is the statsmodels output: {paste}.

Translate for a VP of Sales:

1. What does each coefficient mean in dollars for a one-unit change?
2. Which variables are statistically significant at p < 0.05?
3. What is R-squared and what does it tell us about how much the model explains?
4. What does this model NOT tell us (causality, confounders, missing variables)?
5. What follow-up analysis should we run to test causation?

The last two points are crucial. Regression shows correlation, and AI often lets "significance" imply causation if you are not careful. Always ask explicitly what the model does not say.

Translating Stats Into Stakeholder Language

This is the highest-value prompt you will use as an analyst:

I have this statistical result: {paste output}.

Rewrite for three different audiences:

1. Executive summary (30 words): the one number that matters and whether it is good or bad
2. PM-friendly (80 words): result, confidence, and what to do about it
3. Internal data team (150 words): full methodology, caveats, and follow-ups

Keep the numbers the same across versions. Avoid phrases like "statistically significant" in the first two — translate them into plain language like "we are highly confident" or "we cannot tell the difference."

Four Mistakes AI Makes with Statistics

Watch for these and do not repeat them.

1. Saying "not significant" means "no effect"

A p-value of 0.12 does not prove the effect is zero; it just means the data does not rule out zero. Prompt the AI to say "we cannot detect a difference" rather than "there is no difference."

2. Overstating practical significance

A 0.3% absolute lift can be statistically significant with a huge sample and still be commercially meaningless. Ask AI to report the effect size in dollars or percentage points, not just the p-value.

3. Ignoring the assumption check

Every test has assumptions: normality, independence, homoscedasticity, etc. Ask the AI to list the assumptions and verify them before running the test.

4. Inventing a plausible-sounding number

For in-chat calculations, AI sometimes just estimates. Always run the actual calculation in a code interpreter or manually with scipy, numpy, or statsmodels. Never quote a stat you did not compute.

Bayesian Alternatives

For stakeholders who find p-values confusing, Bayesian A/B analysis is often clearer:

Run a Bayesian A/B analysis on the following conversion data: {paste}. Use a Beta(1,1) prior. Report:

• Posterior distribution parameters for each variant
• Probability that B beats A
• Expected loss of choosing B if A is actually better
• A short plain-language interpretation

"80% probability the new button is better" is easier to explain than "p = 0.04."

Sample-Size Calculators

Before starting any test, compute required sample size:

I want to detect a 5% relative lift from a 3.2% baseline conversion rate with 80% power, alpha = 0.05, two-sided. How many users per variant do I need? Also tell me the minimum detectable effect given the traffic I actually have: 12,000 users per variant per week, running for 2 weeks.

Key Takeaways

• Let AI pick the right test based on data type and independence
• For A/B tests, report CI and effect size, not just p-value
• Regression explains correlation; always ask what it does NOT show
• Translate stats into three versions: exec, PM, data team
• Run calculations in code, never trust in-chat arithmetic
• Bayesian framing is often clearer for non-technical stakeholders