Procedure / Step 1. Always define the events clearly for e.g.; A = buys a phone, B = buys a case. 2. Classify which event it out of the four is mentioned above. Then test it with transaction data by checking whether P(A and B) = P(A) × P(B). 3. Select the correct formula as applicable. 4. Calculate and then validate it against historical sales. 5. Lastly multiply probability by expected customers to get demand. 3. Correct Answer and Interpretation Suppose P(phone) = 0.30, P(case) = 0.40, and 1,000 customers visit. Historically, P(both) = 0.20. • Wrong (addition, ignoring overlap): 0.30 + 0.40 = 0.70 buy at least one item. Correct: 0.70 − 0.20 = 0.50. • Wrong (multiplication, assuming independence): 0.30 × 0.40 = 0.12 buy both. Correct (dependent): 0.30 × P(case | phone), where P(case | phone) = 0.20/0.30 ≈ 0.67, giving 0.20. Consequences of errors: • Overstating “at least one” by 20 points forecasts 700 buyers instead of 500, causing overstocking, tied-up capital, storage costs, and markdowns. • Understating bundle sales (120 instead of 200 buyers of both) causes case stockouts when phones sell, lost revenue, and weaker customer trust. • Errors compound across thousands of products, distorting warehouse space, staffing, and supplier orders. Benefits of correct classification: accurate forecasts, sensible bundle promotions, safety stock placed where demand is linked, and better cash flow. Strategies to Avoid Mistakes • Use a decision checklist: “Can both happen together? Does one affect the other?” before choosing a formula. • Test independence empirically using transaction data rather than assuming it. • Use conditional probabilities and association analysis (such as market-basket analysis) to capture product links. • Build validation checks that flag impossible results, such as probabilities above 1. • Train staff and review models regularly, comparing forecasts with actual sales. Conclusion Picking the kind of event right drastically improves the sales forecasts. The payoff is immediate, you avoid wasting money on excess inventory and can make resource allocation decisions you actually trust.