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<blockquote data-quote="Snarf Zagyg" data-source="post: 8054168" data-attributes="member: 7023840">It depends on the amount of the data. The assumptions that you are using are likely to be incorrect, given a large enough sample size.For example, if you are using just the data set of Critical Role, then it will necessarily be skewed because of the difference between that game and other games.On the other hand, if your data set was the theoretical &quot;all games of 5e, ever,&quot; then it would include expert players, poor players, and average players. Arguing against the data set would be similar to the baseball player who says keep complaining about the one time he was &quot;robbed&quot; of a home run in his career, forgetting that over the long term (4-5 at bats per game, 162 games per year, 15 year career) the sample size of &quot;robbed&quot; vs. &quot;lucky carry over the wall&quot; likely balances out.Or, as I keep saying, there are at least two other good ways to do the math:A. Use actual data; orB. Run repeated Monte Carlo simulations and regression analysis.DPR is a very basic tool, and isn't really &quot;analytics.&quot; It's just a more advanced version of saying, &quot;Should I use a dagger (2.5) or a short sword (3.5)?&quot; It's fine to resolve a binary choice, but it's explanatory power doesn't really work past that.</blockquote>

[QUOTE="Snarf Zagyg, post: 8054168, member: 7023840"] It depends on the amount of the data. The assumptions that you are using are likely to be incorrect, given a large enough sample size. For example, if you are using just the data set of Critical Role, then it will necessarily be skewed because of the difference between that game and other games. On the other hand, if your data set was the theoretical "all games of 5e, ever," then it would include expert players, poor players, and average players. Arguing against the data set would be similar to the baseball player who says keep complaining about the one time he was "robbed" of a home run in his career, forgetting that over the long term (4-5 at bats per game, 162 games per year, 15 year career) the sample size of "robbed" vs. "lucky carry over the wall" likely balances out. Or, as I keep saying, there are at least two other good ways to do the math: A. Use actual data; or B. Run repeated Monte Carlo simulations and regression analysis. DPR is a very basic tool, and isn't really "analytics." It's just a more advanced version of saying, "Should I use a dagger (2.5) or a short sword (3.5)?" It's fine to resolve a binary choice, but it's explanatory power doesn't really work past that. [/QUOTE]

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