So all I need is some feedback for this idea :smiley: Are you in?|||I would say per person hundred runs, this is really doable with a good (good not godly) equiped character, with ten people helping it would already be 1000 runs. I will join in as soon as I get my new hammer going

EDIT: The number of players in the game should also be factored in, as well as other benefits (Getting XP yourself, helping others level etc etc)|||The SPF does this all the time... you can look at result charts if you search for them.|||Naturally, I vote for 400+:smiley:|||The more the better, but then, it is not my time!



I think you need at least 100,000 runs in total to make meaningful statistics about e.g. all uniques which might be considered as statistical evidence that ATMA is correct or wrong and your statistics about really rare ones like Tyrael's or unique, class-specific TC87 armor still won't be very accurate. For that purpose, better consider making a few millions of visits in total.
To be honest, IMO it's a big waste of time. As already said, have a look at the single player forum, that's where the masochist MFers are.|||krischan, are you aware that precision usually goes with the square-root of the number of measurements?
i.e. 100 000 runs isn't 100 better than 1000 runs, but only 10 times as good?|||Well, I studied physics which involved a lot of statistics.

With that logic you can as well say "Why doing 100 runs when it's just 10 times better than a single one ?". No matter if it pays off linearly or not, it's the only way to add precision.
It doesn't depend on the number of events, but on the number of events with the result you are looking for, so when finding let's say one Tyrael's in 100,000 runs, that's an average of 10 in 1,000,000 runs with a standard deviation of sqrt(10)~=3.16. That means if you do several series of a million runs each, the average difference between the expected value and the actual value for each series of 1,000,000 runs has an average of 3.16. If it isn't within certain limits, chances are good that the theory which predicted the value is wrong.
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