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  1. #11
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    with same setup cycle even 10k ped and results would be intresting to see.

    Good data thank you.

  2. #12
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    Quote Originally Posted by GoNi View Post
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    The sample looks a little small.

    Repeat it 100 times, if the numbers are still the same I buy it.

    When you're using these statistical tests, they're designed to use these sample sizes to make predictions of the "true average" you'd get if you did 1000+ drops instead. Those confidence intervals basically mean that if you repeated the study again, 95% of the time the averages would fall within that range. The higher the sample size, the more that confidence intervals shrinks, but the averages generally only bounce around within those limits as you increase sample size.

    Something we never do in science is sample so much that we can get a near direct estimate of the true population size through sheer force. It's expensive and time consuming. If the samples we pull come from a random distribution though (i.e., a random number generator with some set average), 30+ samples is typically enough as long as your comparisons are properly designed and you're using the right statistical tests.

    Quote Originally Posted by Chiee View Post
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    with same setup cycle even 10k ped and results would be intresting to see.

    Good data thank you.
    Likewise, those p-values I give already address that. There's only a 0.0982% chance that 10k ped drops that the negative trend in the graph wouldn't hold true. Similar for the TT test, Leeloo's and my TT would still be expected to be different at that high of a sample size with about 99.7% confidence in that difference. That's the fun thing about research statistics. You can determine what would happen at high sample sizes with much less.

  3. #13
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    Quote Originally Posted by Idiocracy View Post
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    interestingly the second and third miner have roughly a fourth of the hitrate but have about half of the TT

    you could also test this with doing ore once and enmatter once, with a few days inbetween. i assume the higher TT in regards to hitrate is due to more ore hits. could be luck but could also be a statistically significant difference
    I'm really cautious about directly comparing the actual numbers from TT because of the need for that zero-inflated model I mention. With that statistical tests, I can definitely say miner 1 had more than miner 2 or 3, but the way the model quantifies that doesn't boil it down into simple math we can directly compare to hit rate. Those total TTs I gave pretty much can only give a general idea, but they have the same problem as averages not being "correct" because of all the zero data.

    As for type affecting TT (just happening to get more ores with higher TT than enmatter claims), I forgot to mention I did look at that. I can actually account for that in the model as a covariate. That effect doesn't change the model at (p = 0.1911), so the results pretty much stay the same.

  4. #14
    Thank you (all three of you) for this interesting experiment. At the very least, it now satisfies my nagging query as to why I can sometimes get up to 20 NRFs in a row while mining planetside.

    ----------------------------------
    Going off on a tangent now though
    ----------------------------------
    After reading this, two additional questions now pop up.
    1. Is it safe to conclude (from this) that there is at least a 2D (x,y) spatial distribution to the mining claims? If yes, it would then lead me to wonder if there isn't a 3rd dimension (z) to its distribution. Would going through with this again with everything done the same way but using three different avg. search depth finders, but with the same search radius, result in any difference in the hit rates?
    2. How much time in between (the drops) do you think it would require before there is no longer any interference (in hit rates) between the three miners? 5 minutes and 10 minutes is definitely out right? So maybe 30 minutes, or perhaps even 1 hour?


    Lastly, it might probably just be smoke and mirrors, but I can't help associating your hit rates in this particular manner/pattern.
    • 1st Miner (Hit Rate): 26.7%
    • 2nd Miner (Hit Rate): 26.7% * 26.7% = 7.1289% (in comparison to the 6.7%)
    • 3rd Miner (Hit Rate): 26.7% * 26.7% * 26.7% = 1.9034% (in comparison to the 5%)

    Its almost like having the probability of hitting a claim to be around 26.7% and then the 2nd/3rd Miners' to be analogous to trying to hit 2/3 consecutive (in a row) claims in the exact same spot. And any discrepancy (in hit rates) can be explained off due to "luck" and "server's regeneration of claims".

    ------------------
    As for the TT part
    ------------------
    Would it be possible to separate the tally for number of claims and TT of enmatter and ores? Then do an average to see if there's a difference in TT values (of the enmatt and ore claims) between the three miners.

    It would be interesting to see if MA does any "compensation" to the 2nd and 3rd miners for their "reduced hit rates".

    Last edited by sawachika; 02-25-2019 at 10:00.

  5. #15
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    Quote Originally Posted by sawachika View Post
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    Thank you (all three of you) for this interesting experiment. At the very least, it now satisfies my nagging query as to why I can sometimes get up to 20 NRFs in a row while mining planetside.

    ----------------------------------
    Going off on a tangent now though
    ----------------------------------
    After reading this, two additional questions now pop up.
    1. Is it safe to conclude (from this) that there is at least a 2D (x,y) spatial distribution to the mining claims? If yes, it would then lead me to wonder if there isn't a 3rd dimension (z) to its distribution. Would going through with this again with everything done the same way but using three different avg. search depth finders, but with the same search radius, result in any difference in the hit rates?
    2. How much time in between (the drops) do you think it would require before there is no longer any interference (in hit rates) between the three miners? 5 minutes and 10 minutes is definitely out right? So maybe 30 minutes, or perhaps even 1 hour?


    Lastly, it might probably just be smoke and mirrors, but I can't help associating your hit rates in this particular manner/pattern.
    • 1st Miner (Hit Rate): 26.7%
    • 2nd Miner (Hit Rate): 26.7% * 26.7% = 7.1289% (in comparison to the 6.7%)
    • 3rd Miner (Hit Rate): 26.7% * 26.7% * 26.7% = 1.9034% (in comparison to the 5%)

    Its almost like having the probability of hitting a claim to be around 26.7% and then the 2nd/3rd Miners' to be analogous to trying to hit 2/3 consecutive (in a row) claims in the exact same spot. And any discrepancy (in hit rates) can be explained off due to "luck" and "server's regeneration of claims".

    ------------------
    As for the TT part
    ------------------
    Would it be possible to separate the tally for number of claims and TT of enmatter and ores? Then do an average to see if there's a difference in TT values (of the enmatt and ore claims) between the three miners.

    It would be interesting to see if MA does any "compensation" to the 2nd and 3rd miners for their "reduced hit rates".

    As King already mentioned we are going to do a lot more testing, the more questions, the more testing

    About the repop or respawn for claims, and now I speak from my own experience only, I wait 2 hours before redoing the same zone over and over again. I tried shorter times, but then my HR/TT return was less, a lot less.
    If this is the same on any server or for other resources I never tested them all.
    Those I know who mine the same way, try already after 1,30 hours but they go for specific ores most of the time.

    Another thing that is worth mentioning is that you never can say that you didn't find anything. Either you will find a claim some 1 has missed, most likely because he/she didn't drop a 2nd bomb at the same place (rebombing) or there is a safety barrier that you will get a minimum if you keep mining in that zone.
    Then I'm thinking of around 10-20% HR or even 25%
    Either way if you get a HR of 25-30% you should consider stop mining there or keep a very close eye on your TT return so it doesn't go under 90-80% (IMO)
    These % are all for mining without amps/enhancers
    Last edited by LeelooM; 02-25-2019 at 12:39.
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  6. #16
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    Quote Originally Posted by sawachika View Post
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    Thank you (all three of you) for this interesting experiment. At the very least, it now satisfies my nagging query as to why I can sometimes get up to 20 NRFs in a row while mining planetside.

    ----------------------------------
    Going off on a tangent now though
    ----------------------------------
    After reading this, two additional questions now pop up.

    ------------------
    As for the TT part
    ------------------
    Would it be possible to separate the tally for number of claims and TT of enmatter and ores? Then do an average to see if there's a difference in TT values (of the enmatt and ore claims) between the three miners.

    It would be interesting to see if MA does any "compensation" to the 2nd and 3rd miners for their "reduced hit rates".

    Leeloo covered the rest pretty well. For the question of TT, I addressed this same question in my reply before yours. When you add resource type (ore vs. enmatter) as a covariate in the model, you can account for the fact that each type has a different base TT claim size. Accounting for that difference doesn't change the results (p = 0.1911) on TT. So basically, no compensation.

    I mentioned it in another thread, but when you pull random samples, you are going to get "streaks" as a part of that randomness (10 heads in a row in a coin flip). I'm pretty convinced that this is what is going on when people see "compensation" happening for a bad run. It's a well known problem that people looking at raw data without appropriate statistical tests will see such trends in the randomness and think they truly exist. If someone repeated the exact same study a bunch of times or with a single higher sample size, they could test the "compensation" question further and rule out that randomess, but I'm planning to focus on the other things you mentioned that are also cheaper to test. I'll probably draw up a new thread in a few days on the next round.

  7. #17
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    Thank you very much for doing this experiment and for sharing the results.

    Regarding this part:

    Quote Originally Posted by kingofaces View Post
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    Something we never do in science is sample so much that we can get a near direct estimate of the true population size through sheer force. It's expensive and time consuming. If the samples we pull come from a random distribution though (i.e., a random number generator with some set average), 30+ samples is typically enough as long as your comparisons are properly designed and you're using the right statistical tests.
    That is only valid with some strong assumptions: independence of the observations, low variance and some sort of stationarity (i.e. that results do not depend on time and location). I am not sure any of these assumptions hold in this case, so I would not claim that the p-value for example is very accurate. Of course, the effect size is very convincing and would likely result in a high significance with a more elaborate model.

    I find your analysis and conclusion quite convincing but I think it can be strengthened if this test repeated in different setting: different locations/times, wider time intervals and more avatars (easy to say, as I have barely time to play and cannot offer any help).

    Again, thanks and keep up the good work.

  8. #18
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    Quote Originally Posted by Kosh View Post
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    Thank you very much for doing this experiment and for sharing the results.

    Regarding this part:



    That is only valid with some strong assumptions: independence of the observations, low variance and some sort of stationarity (i.e. that results do not depend on time and location). I am not sure any of these assumptions hold in this case, so I would not claim that the p-value for example is very accurate. Of course, the effect size is very convincing and would likely result in a high significance with a more elaborate model.

    I find your analysis and conclusion quite convincing but I think it can be strengthened if this test repeated in different setting: different locations/times, wider time intervals and more avatars (easy to say, as I have barely time to play and cannot offer any help).

    Again, thanks and keep up the good work.
    Thanks for reply and those test will be coming up in the next weeks when everybody can
    MINER'S DREAM
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    Depth Enhancers
    Ores - Enmatters - Treasures

  9. #19
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    Quote Originally Posted by Kosh View Post
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    Thank you very much for doing this experiment and for sharing the results.

    That is only valid with some strong assumptions: independence of the observations, low variance and some sort of stationarity (i.e. that results do not depend on time and location). I am not sure any of these assumptions hold in this case, so I would not claim that the p-value for example is very accurate. Of course, the effect size is very convincing and would likely result in a high significance with a more elaborate model.

    I find your analysis and conclusion quite convincing but I think it can be strengthened if this test repeated in different setting: different locations/times, wider time intervals and more avatars (easy to say, as I have barely time to play and cannot offer any help).

    Again, thanks and keep up the good work.

    Some of that is addressed in my next round of testing thread, but the model assumptions are a big one (and I was the oddball who actually liked research statistics in grad school, so I love to talk them).


    1. Independence of observations shouldn't really be an issue unless someone is going to claim what you found in previous drops affects your later hit rate (or some form of autocorrelation). I didn't find any evidence that something like that was going on when looking at the model data.

    2. Low variance isn't a requirement, but maybe you meant homogeneity of variance is (i.e., not having highly variable variances)? The logistic regression is already tailored to dealing with this to a degree, and I didn't find any evidence of overdispersion either as another check on that.

    3. The time factor would largely be minimized by us all testing in a fairly short window. For a location ( x% higher or lower hit rates for all of us), that would really matter for the purposed of this experiment. That or an interaction effect (miner 1 stays the same, but miner 2 and 3 have even lower hit rates), would get more into fine tuning if you wanted to really model exactly when and where someone should mine. If this were a full blown field study where I'd be putting out recommendations like that, I'd definitely want more locations. Just for showing that such an effect can happen though, this design did the trick, and it'll be validated to some degree in the next round of testing.


    I'm glad people are having fun poking around on this subject, so I'll keep things going for a bit.

  10. #20
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    Thanks guys! Very interesting.

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