During my PhD studies I came across with a very large number of tasks that my supervisor assigned to me. Many of them were quite demanding, such as a new code development/extension. For these I knew that I would need a couple of days, even weeks to accomplish. Yet other tasks seemed relatively easy to complete while discussing them with my supervisor, but as soon as I went back to my desk, a million problems and questions came up. Most of the time it as conceptual issues, for example I had doubts about the correctness of an argument on which the whole idea was based. This means that I had to rethink of everything I’ve discussed with my supervisor in order to understand fully what we were talking about and make sure that it was the right thing to do. Then there were technical details that I had to take into account, which I hadn’t considered at first sight.

The topic of effort underestimation is widely studied and is part of a phenomenon known as the planning fallacy.

Numerous times it was these details that required additional effort. Talking about something in theory seems easy, but the real challenge begins as soon as a hands-on exercise is undertaken. And yet some other times I just did not have at hand the correct way of actually performing the task, e.g. how to write a programming code required to perform the task. The latter, in particular, involved additional research and of course considerable amounts of time, but it was partly enjoyable, as I had the chance to learn something new.

In fact, the topic of effort underestimation is widely studied and is part of a phenomenon known as the planning fallacy. Wikipedia features an interesting article on the planning fallacy. This phenomenon has further implications in cost planning (where costs for a particular project tend to be higher than expected) and benefit shortfalls (where benefits from a particular action tend to be lower than expected).

The point here is that in general you should not underestimate the effort required for a particular task, and don’t fall in the fallacy that nothing will go wrong. This additional effort will help you become more acquainted with the tools (be it programming packages/languages, machinery, lab instruments, etc) that you are using/learning. This in fact is part of your research. So, always grasp the chance to learn anything different from what you already know and use, despite the extra time that this might require. As an additional tip, bear this in mind if your supervisor asks for an estimate to have a task ready so that you add some more time. 🙂