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.

📊 Turning Raw Retail Data Into Business Insights

I recently worked on a retail sales dataset to demonstrate how raw business data can be transformed into useful insights.

My process included:

✅ Data cleaning and validation ✅ Revenue calculations ✅ Sales performance analysis ✅ Product and store analysis ✅ Excel/Power BI visualization ✅ Identifying actionable business insights

The analysis answers questions such as:

• Which store generates the most revenue? • Which products drive sales? • Which products sell the most units? • Which salesperson performs best? • How do sales change over time?

Tools: Excel | Power BI | Python/Pandas | SQL

Clean data is the foundation. Good analysis turns that data into decisions.

#DataAnalysis #DataCleaning #Excel #PowerBI #Python #SQL #DataAnalytics #Freelance