How many observations were made regarding competitor bids in previous auctions?

• A. 260 observations
• B. 280 observations
• C. 300 observations
• D. 320 observations

Answer: (B)

When calibrating a Monte Carlo model of competitor bidding, you want enough historical observations to accurately estimate how bids vary, without overdoing it. A sample that is too small leads to high sampling error—the estimated bid distribution will swing a lot with each new auction, making the simulation results noisy and less reliable. On the other hand, an excessively large set can be unnecessary and may even introduce bias if data quality changes over time or if markets evolve, while simply adding more data can waste computational effort. The best choice is a moderate, representative set of prior auctions that smooths out random fluctuations and gives a stable view of typical bidding behavior. This balance improves the reliability of the simulated outcomes. The other options either risk unstable estimates due to too little data or incur diminishing returns from data that’s larger than needed.