For example, suppose you’re planning to put together a new computer from parts you order online. You’ve ordered the parts, and feel that this time you know most of the common hiccups of building the machine. You estimate that it will take you two weeks to complete. However, in the past you’ve built three computers – and they took 3, 5 and 4 weeks, respectively. Once the parts came in later than expected, once you were at work too much to manage the build and once you had some issues that needed resolving. But this time is different!
Now, the inside view says you feel confident that you’ve learnt from you mistakes. Therefore, estimating less build time than in history seems to make sense. The outside view, on the other hand, says that even if you have learnt something, there have always been hiccups of some kind – so that is likely to happen again. Hence, the outside view would estimate your build time to be around the average of your historical record.
In such a simple case it’s quite easy to see why taking the outside view is sensible, especially now that I’ve painted the inside view as a sense of “I’m better than before”. Unfortunately, real world is not this clean, but much messier. In the real world, the question is not should you use the outside view (you should), but which one? The problem is that you’ve often got several options.
For example, suppose you were recently appointed as a project manager in a company, and you’ve led projects for a year now. Two months ago, your team got a new integration specialist. Now, you’re trying to think how much time it would be to install a new system to a very large corporate client. You’d like to use the outside view, but don’t know which one. What’s the reference point? All projects you’ve ever led? All projects you’ve led in this company? All projects with the new integration specialist? All projects for a very large client?
As we see, picking the outside view to use is not easy. In fact, this problem – a deep philosophical problem in frequentist statistics – is known in statistics and philosophy as the reference class problem. All the possible reference class in this example make some sense. The problem is that of causality: you have incomplete knowledge about which attributes impact your success, and how much. Does it matter that you have a new integration specialist? Are these projects very similar to ones you’ve done at the previous company? How much do projects differ by client size? If you can answer all these questions, you’d know which reference class to use. But if you knew the answers to these, you probably won’t need the outside view in the first place! So what can you do?
A practical suggestion: use several reference classes. If the estimates from these differ by a lot, then the situation is difficult to estimate. But hopefully finding this out improves your sense of what are the drivers of success for the project. If the estimates don’t diverge, then it doesn’t really matter which outside view you pick, so you can be more confident of the estimate.