Final answer:
An unbiased forecast implies that the forecast errors average out to zero, but it doesn't ensure a small magnitude of error without low variance. It's essential to have tight error bounds for precise forecasts, use one significant figure, and check the reasonableness of estimations.
Step-by-step explanation:
When a forecast is said to be unbiased, the expected value of the forecast errors is zero. This means that over many forecasts, the errors will not systematically be too high or too low; they will be equally distributed above and below the actual values. The magnitude of the forecast error, then, should be small to indicate a precise forecast. However, a small magnitude of error is not guaranteed just by a forecast being unbiased; it must also have a small variance or standard deviation. To ensure a more precise forecast, it's beneficial if the magnitude of the error bounds is tight, meaning the forecast is close to the actual values.
When making estimations, it's important to maintain simplicity. Using just one significant figure (sig. fig.) is usually adequate for ballpark estimates, as the exact value is not as important as being in the correct order of magnitude. Lastly, one should always check the reasonableness of an answer by comparing it with known quantities and using logical reasoning to ensure no fundamental mistakes have been made.