In probability, increasing the number of independent exposure units generally improves forecast accuracy. Which choice best expresses this principle?

In probability, increasing the number of independent exposure units generally improves forecast accuracy. Which choice best expresses this principle?

• A. Increasing the number of independent exposure units.
• B. Decreasing data variability.
• C. Reducing data quality.
• D. Narrowing the time window.

Answer: (A)

Increasing the number of independent exposure units provides more information, and the average of many independent observations becomes a more reliable estimate of the true outcome. As you add independent units, the variability of the sample mean decreases, so the forecast gets closer to the actual mean. This is the essence of why larger samples improve precision: the random fluctuations tend to cancel out, narrowing the error around the expected value. The other options don’t express this mechanism. Decreasing data variability is not guaranteed by any single action and doesn’t explain why more data points help. Reducing data quality obviously harms forecasts, not improves them. Narrowing the time window reduces the amount of data available and typically increases variability, not improves accuracy.

Increasing the number of independent exposure units provides more information, and the average of many independent observations becomes a more reliable estimate of the true outcome. As you add independent units, the variability of the sample mean decreases, so the forecast gets closer to the actual mean. This is the essence of why larger samples improve precision: the random fluctuations tend to cancel out, narrowing the error around the expected value.

The other options don’t express this mechanism. Decreasing data variability is not guaranteed by any single action and doesn’t explain why more data points help. Reducing data quality obviously harms forecasts, not improves them. Narrowing the time window reduces the amount of data available and typically increases variability, not improves accuracy.