Data Science and Statistics for Decision Making

Data science uses data to answer questions and guide choices. Statistics adds a disciplined view of what the data can tell us and what it cannot. Together they help leaders see evidence, compare options, and learn from outcomes rather than rely on guesswork.

Why this approach matters

A clear decision question keeps work focused. Frame the problem, define success, and set acceptable risk. Then gather data, clean it, and look for patterns with simple visuals.

A practical workflow

• Frame the decision
• Collect and clean data
• Explore with descriptive stats and visuals
• Model or test ideas
• Quantify uncertainty with intervals or probabilities
• Make a decision and plan monitoring

A simple example

A retailer tests a new email campaign. Over 4 weeks, 5,000 recipients are split 50/50. Baseline conversions: 2.0% (50 sales). After the campaign: 2.6% (130 sales). The lift is 0.6 percentage points. The quick test shows moderate evidence, but it is not final proof. If the business needs high certainty, gather more data or extend the test.

Common cautions

Data quality, bias in sample, and the risk of chasing shiny results. Avoid overfitting, p-hacking, and trusting a single chart. Communicate findings with plain language and simple visuals.

Key Takeaways

• Frame the decision and define success.
• Quantify uncertainty and avoid overclaiming.
• Communicate results clearly and link decisions to data.