Programmers are often asked to accurately estimate tasks that are too large to estimate. The smaller or larger the task, the more likely it is that there will be errors in the estimate. The larger a task, the more likely there is a detail that has been missed during estimation, the more chance that you will encounter a surprise during development that greatly increases development time.

Most successful estimation methods boil down to refusing the estimate larger tasks, and instead breaking them down into smaller tasks. The process of breaking them down forces people to think through them in more detail, and you flush out many of the surprises early on. Similarly, most of the successful systems require you to apply a minimum amount of time per task (e.g. one hour). This removes the problem of programmers underestimating simple tasks.

The best estimates come when you continue to use that system over several iterations of work, and analyze the accuracy of previous estimates. This gives you (and other developers) a better sense of how to make good estimates.

Sadly, even when you have a reliable system of predicting how much work a development team can get done in a given chunk of time, many managers bow to pressure from above to hit deadlines, and expect the team to accomplish more than the data says they are able to accomplish.