What makes an A/B test readout useful to a product team?

My preferred first page answers four questions:

What changed, and by how much?

How uncertain is the estimate?

Did an important guardrail get worse?

What decision does the evidence support, and what remains unresolved?

A result can be statistically significant and still too small to matter. An inconclusive result can still leave a meaningful gain or loss plausible. The decision needs more than a green badge.

I built an independent A/B analyzer to make these checks easier to inspect. The project includes a live demo, downloadable reports and documented limitations: https://contra.com/p/mgoXb3qY-ab-test-analyzer-correct-p-values-power-and-fdr

If you work on product or CRO, what do you wish analysts put on the first page more often?

This is a strong framing of experiment readouts: separating signal, uncertainty, guardrails, and the actual decision keeps the team honest. The reminder that significance is not the same as usefulness is especially important.

Budget vs. Actual Analysis

This project analyzes a small retail brand's Q1 2026 performance by comparing budgeted figures against actual revenue and expenses. I built a fully formula-driven Excel model — no hardcoded numbers — that automatically calculates dollar and percentage variance for each category, flags results as "Favorable" or "Unfavorable" with color-coded formatting, and rolls everything up into Total Revenue, Total Operating Expenses, and Net Operating Income. A clustered bar chart visualizes budget vs. actual side-by-side for quick executive review. The goal was to show how a business owner could spot exactly where they overspent or outperformed expectations at a glance, without digging through raw numbers.