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> If your decide your confidence intervals are described by classical statistics, then adding them won't change anything on the outcome. I'm trying to underst

by kqr 4y ago

> If your decide your confidence intervals are described by classical statistics, then adding them won't change anything on the outcome. I'm trying to understand what you mean by this. Are you referring to the fact that for thin tails, the sum of expectations will grow significantly faster than the standard deviation, and thus the 90th percentile will, relatively speaking, tend toward the expectation with more tasks? (I.e. an appeal to the LLN.)

kelseyfrog 4y ago
I believe what the parent is referring to is that the sum of two normally distributed variables can be modeled as normally distributed[1]. Ie: normal distributions are closed under addition. This fact leads to one's naive idea about adding variances working as expected. However, in task estimation[duration] aren't normally distributed - they're much more log-normally distributed, and you can't simply add log-normal distribution parameters in the same way[2]. Instead he log-normal distribution is (largely) closed under multiplication which is fairly useless when we want to add tasks to determine total time. Moreover, the reductive conceptualization of normally distributed estimations leads people to the erroneous communication of "X plus or minus Y". The assumption is that the bounds are symmetric(they aren't). The chance that the estimate is higher than the mode is much greater than it is lower than the mode. As a data scientist, I want to say that the model for task estimation is over-reduced. Even so, I feel like the push-back I'd get from suggesting that folks estimate in log-space would be that I'm being ridiculous complicated. 1. https://en.wikipedia.org/wiki/Sum_of_normally_distributed_random_variables https://en.wikipedia.org/wiki/Sum_of_normally_distributed_ra... 2. See the Fenton-Wilkinson approximation.
- kqr 4y ago
  Adding variances is not a naive idea but a fundamental property of variances! And I don't think that's what GP means because they bring up heavy tails as a point separate from "classical" methods -- and the lognormal distribution is heavy-tailed under appropriate parameters.
- dragonwriter 4y ago
  > Even so, I feel like the push-back I'd get from suggesting that folks estimate in log-space would be that I'm being ridiculous complicated Its a more theoretically grounded relative of the common agile “T-shirt sizes quantified by Fibonacci sequence, without the repeated ‘1’” estimation recommendation (which is pretty close to just doing “each complexity step is ×1.5”.)
marcosdumay 4y ago
Yes, it's about the LLN. At the end you will get an error proportionally smaller than a typical confidence interval that everybody already has an intuition for. You won't add meaningful information to the analysis.

by kqr 4y ago

kelseyfrog 4y ago

I believe what the parent is referring to is that the sum of two normally distributed variables can be modeled as normally distributed[1]. Ie: normal distributions are closed under addition. This fact leads to one's naive idea about adding variances working as expected. However, in task estimation[duration] aren't normally distributed - they're much more log-normally distributed, and you can't simply add log-normal distribution parameters in the same way[2]. Instead he log-normal distribution is (largely) closed under multiplication which is fairly useless when we want to add tasks to determine total time. Moreover, the reductive conceptualization of normally distributed estimations leads people to the erroneous communication of "X plus or minus Y". The assumption is that the bounds are symmetric(they aren't). The chance that the estimate is higher than the mode is much greater than it is lower than the mode. As a data scientist, I want to say that the model for task estimation is over-reduced. Even so, I feel like the push-back I'd get from suggesting that folks estimate in log-space would be that I'm being ridiculous complicated. 1. https://en.wikipedia.org/wiki/Sum_of_normally_distributed_random_variables https://en.wikipedia.org/wiki/Sum_of_normally_distributed_ra... 2. See the Fenton-Wilkinson approximation.

kqr 4y ago
Adding variances is not a naive idea but a fundamental property of variances! And I don't think that's what GP means because they bring up heavy tails as a point separate from "classical" methods -- and the lognormal distribution is heavy-tailed under appropriate parameters.
dragonwriter 4y ago
> Even so, I feel like the push-back I'd get from suggesting that folks estimate in log-space would be that I'm being ridiculous complicated Its a more theoretically grounded relative of the common agile “T-shirt sizes quantified by Fibonacci sequence, without the repeated ‘1’” estimation recommendation (which is pretty close to just doing “each complexity step is ×1.5”.)

marcosdumay 4y ago

Yes, it's about the LLN. At the end you will get an error proportionally smaller than a typical confidence interval that everybody already has an intuition for. You won't add meaningful information to the analysis.