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The interesting thing is that by the central limit theorem, the mean of a mean is normally distributed. This is extremely helpful. Here's what I suggest you d

The interesting thing is that by the central limit theorem, the mean of a mean is normally distributed. This is extremely helpful. Here's what I suggest you do: Same size your stories to small values. Do 30 stories in a sprint and take the mean. Do 30 sprints and take the mean of the sprint. What you get is the mean amount of time to do a sprint of 30 stories. What's amazing is that this estimate will be normally distributed. You can measure the variance to get error bars. Of course 900 stories to get good estimates ;-) However, imagine that your stories averaged 2 days each. Imagine as well that you have a team of 10 people. That means that you will finish a "sprint" of 30 stories in 6 days (on average). 30 sprints is 180 days -- the better part of a year, but you probably don't need a 95% confidence interval. You will find that after a few sprints, you'll be able to predict the sprint length pretty well (or if you set your sprints to be a certain size, then you will predict the number of stories that will fit in it, with error bars). The other cool thing is that by doing this, you will be able to see when stories are outliers. This is a highly undervalued ability IMHO. Once a story passes the mean plus the variance, you know you've got a problem. Probably time to replan. If you have a group of stories that are exceeding that time, then you may have a systemic estimation problem (often occurs when personnel change or some kind of pressure is being applied to the team). This kind of early warning system allows you to start trying to find potential problems. This is really the secret behind "velocity" or "load factor" in XP. Now, does it work on a normal team? In my experience, it doesn't because groups of people are crap at calmly using statistics to help them. I've had teams where they were awesome at doing it, but that was the minority, unfortunately.

piccolbo 8y ago
The central limit theorem is in the limit for the number of variables in the sum approaching infinity. In the finite world, the article explains how it's done. The article is saying, the sum of lognormals is not normal. You are saying: take enough of them and it is normal. The article is still more accurate than your reasoning for 30 stories. From the wikipedia entry for Central limit theorem " As an approximation for a finite number of observations, it provides a reasonable approximation only when close to the peak of the normal distribution; it requires a very large number of observations to stretch into the tails". To prduce a 95% confidence intervals, you have to upper-bound the tails. All methodologies that are based on sum of subtasks estimates are not evidence based. But we knew already sw methodologies are not evidence-based, did we?
- ska 8y ago
  You are saying: take enough of them and it is normal. This doesn't completely undermine your point, but that isn't what they are saying, I think. I read it as saying by CLT that the estimates of the mean of those distributions is normal and centered on [the mean you are actually interested in]. Tails are perhaps somewhat a red herring here, because you don't really care about them unless you are specifically trying to evaluate worst-case-but-really-unlikely.
  - mikekchar 8y ago
    Yes, that is correct. It's been a very long time since I studied statistics, so I'm not sure if the variance of a mean has the same confidence interval as the mean. I suspect not. So you would indeed need to have a very large number of samples to get good error bars. It's a good point which I hadn't really considered. However it will never really get that far anyway because hopefully you'll intervene before the long tail hits you. I think those really long tails are more of a problem when you are working with "features" that are much longer. If you have 1 day stories and you've been working on the story for a whole week, you know you have a massive problem. It's time to back up and see if there is a way to break it up, or to do it differently. If you have a feature that is a month, by the time you get to 5 months, you have so much capital invested in the original plan that it's very hard (politically) to say, "Nope... this isn't working out. Let's try something else". Of course, it is very hard to get your organisation to plan to a 1 day level of granularity.

piccolbo 8y ago

The central limit theorem is in the limit for the number of variables in the sum approaching infinity. In the finite world, the article explains how it's done. The article is saying, the sum of lognormals is not normal. You are saying: take enough of them and it is normal. The article is still more accurate than your reasoning for 30 stories. From the wikipedia entry for Central limit theorem " As an approximation for a finite number of observations, it provides a reasonable approximation only when close to the peak of the normal distribution; it requires a very large number of observations to stretch into the tails". To prduce a 95% confidence intervals, you have to upper-bound the tails. All methodologies that are based on sum of subtasks estimates are not evidence based. But we knew already sw methodologies are not evidence-based, did we?

ska 8y ago
You are saying: take enough of them and it is normal. This doesn't completely undermine your point, but that isn't what they are saying, I think. I read it as saying by CLT that the estimates of the mean of those distributions is normal and centered on [the mean you are actually interested in]. Tails are perhaps somewhat a red herring here, because you don't really care about them unless you are specifically trying to evaluate worst-case-but-really-unlikely.
- mikekchar 8y ago
  Yes, that is correct. It's been a very long time since I studied statistics, so I'm not sure if the variance of a mean has the same confidence interval as the mean. I suspect not. So you would indeed need to have a very large number of samples to get good error bars. It's a good point which I hadn't really considered. However it will never really get that far anyway because hopefully you'll intervene before the long tail hits you. I think those really long tails are more of a problem when you are working with "features" that are much longer. If you have 1 day stories and you've been working on the story for a whole week, you know you have a massive problem. It's time to back up and see if there is a way to break it up, or to do it differently. If you have a feature that is a month, by the time you get to 5 months, you have so much capital invested in the original plan that it's very hard (politically) to say, "Nope... this isn't working out. Let's try something else". Of course, it is very hard to get your organisation to plan to a 1 day level of granularity.