Tree of Savior Forum

[DropPerKill][Work in Progress] Part 1: RNG or DPK? Experiment to Prove if Drop Chance is RANDOM or NON-RANDOM

General Discussion
#27

I wouldn’t worry about it too much, CookyKim. I appreciate the time and effort you’ve put into your data (as well as the time you’ve invested in your posts and your willingness to delve into statistics). I believe that the readers can draw their own conclusions from the data you’ve gathered.

I have no doubt that your conclusion is correct.

Mathinator is likely professionally involved with statistics and he probably also does this as a hobby in his spare time. Based on his knowledge and confidence of the subject, I’d estimate that he is likely aged somewhere in the late forties or even upper fifties. The information contained in his post is definitely not a mishmash of copypasta found around the internet; it seems to be written with knowledge based on direct personal experiences.

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#28

That is what he tries to portray, but that still doesn’t change the fact that he has so far came into this thread and repeatedly calling “nonsense” everywhere, meanwhile providing no actual suggestion for improvement. This is what bugs me. You can be an expert but that doesn’t give you the right to behave like a jerk.

He constantly insist that I do not understand what a 95% confidence level means. Yet he has not helped me or the readers understand its actual meaning besides linking to a Wikipedia article. Look if Wikipedia was actually effective in dispelling such misunderstandings, then the misunderstanding would not have occurred in the first place. I have read the wikipedia article among other sites on the web. The information is conflicting and doesn’t always pertain to the topic at hand.

Next, what is this guy’s credentials? Does he truly have credibility? I do not know this internet “expert wanna be”. He has so far offered no link to his reddit persona. He has not demonstrated superior statistical knowledge, besides throwing jargons everywhere, and saying everyone else is wrong. In fact only probability he calculated:

Was flat out wrong. I still do not know how he got that 8% but it is still guaranteed to be wrong because he did not understand the data. It was not 190 failures with a success in ~40 trials. In addtion what kind of expert makes crappy assertions like that? With no actual math to back up his claim?

Finally, he repeatedly insist that something can be done to improve this thread but does not actually want to do anything:

If any of you actually learned something from his posts, or understands the direction he wants me to take to improve the methodology, then please clarify it for me.

So we have an internet self proclaimed expert that goes about different threads boosting his ego, and pisses on people who (naturally) suspect his expertise? Who gave him the right and credentials to go about trashing other people’s thread?

This must be the most ridiculous expert I’ve (not) met in my life. All the experts that I’ve seen are humble and helpful, but this one is arrogant and abrasive.

With that I am going to head over to our local university and ask for help from a math professor that I know. It will take a longer time to get answers but at least it won’t be something ridiculous like this @Mathinator guy.

Edit:

From wikipedia:

The Clopper-Pearson interval is an early and very common method for calculating binomial confidence intervals.[6] The Clopper-Pearson interval is an exact interval since it is based directly on the binomial distribution rather than any approximation to the binomial distribution. This interval never has less than the nominal coverage for any population proportion, but that means that it is usually conservative. For example, the true coverage rate of a 95% Clopper-Pearson interval may be well above 95%, depending on n and θ. Thus the interval may be wider than it needs to be to achieve 95% confidence. In contrast, it is worth noting that other confidence bound may be narrower than their nominal confidence width, i.e., the Normal Approximation (or “Standard”) Interval, Wilson Interval,[3] Agresti-Coull Interval,[8] etc., with a nominal coverage of 95% may in fact cover less than 95%.[2]

So he goes about dissing a commonly used interval - saying it’s out of favor but not actually explaining why it is not appropriate to use in this scenario. Nor has he pointed the correct way to do this. All he claims is “oh this is so outdated, you’re a noob”. Ok, so helpful.

Agresti-Couli interval (adjusted wald)
Interval for 999 trials:

0.1777% - 1.203% (Clopper-Pearson: 0.1627% - 1.164%)

Interval for Wizard:

-0.0831% - 0.4859% (Clopper-Pearson: 0 - 0.3876%)

Interval for Cleric:

5.134% - 27.17% (Clopper-Pearson: 4.297% - 27.30%)

I think I’m getting seriously trolled here. Just putting “Math” in his name and drawing random jargon and he thinks he’s an expert.

From wikipedia:

the confidence interval for the true proportion innate in that coin is a range of possible proportions which may contain the true proportion. A 95% confidence interval for the proportion, for instance, will contain the true proportion 95% of the times that the procedure for constructing the confidence interval is employed. Note that this does not mean that a calculated 95% confidence interval will contain the true proportion with 95% probability. Instead, one should interpret it as follows: the process of drawing a random sample and calculating an accompanying 95% confidence interval will generate a confidence interval that contains the true proportion in 95% of all cases. The odds that any fairly drawn sample from all cases will be inside the confidence range is 95% likely, so there is a 5% risk that a fairly drawn sample will not be inside a 95% confidence interval.

So tell me how have I misinterpreted the actual meaning of confidence interval? have I ascribed probabilistic meaning to the interval? If so quote me on it.

According to the Wikipedia’s:

The process of drawing a random sample and calculating an accompanying 95% CI…
Will generate a confidence interval that contains the true proportion 95% of all cases

I have generated confidence intervals that contains the true proportion (100% probability) (95% of the cases will be correct), and I have demonstrated that the proportions of the 3 tests did not agree, and concluding that the drop system is not a simple RNG. Exactly the same logic I’ve stated previously:

Whereas your suggestion was this:

The confidence interval means that the true population proportion, p, is within that interval. This statement is true 95% of the time. I have not said anything about the probability of the true population proportion, p, being in or outside of this interval in the OP.

Except that this confidence interval isn’t an estimator, and that I have assigned no probability in the original post at all. The more I learn about this topic the more BS your posts seem to be.

I’m beginning to think that @Mathinator isn’t a real statistics expert, but a forum dweller that only knows some key aspects of statistics and the common mistakes that are made within this field. So all he can do is find flags but is unable to actually derive any original work or ideas. You know, like an error checking machine - only able to find errors, but unable to compose or understand the original work.

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#29

I just want a running shot gem but its impossible on Klaipeda b/c the large rmt guilds will just monitor each channel to see which is being farmed, they calculate that 300 are bring killed an hour, and they estimate the day the dpk window will open and on that day they send 4 of their guildess out to dibo each spawn and they usually have 2 bots running inbetween each half of the spawns aswell. They wont touch a single dbscap archer until someone else kills 8-9k, really fkng broken, I would love this dpk to be speculation but if its all BS then why does this happen EVERY maintenance/week, that’s not a coincidence.

#30

What has this thread become. I believe in DPK since it helps me get items. And I believe you too, Cookykim.

#31

This system is indeed broken and easily exploited. Which is why I spent my entire weekend on this project - and now maybe even more time to validate it. I also really hope that hkkim will look into this issue, because it is downright frustrating to players who want to farm rares without being kicked in the rear.

@greyhiem
Thanks for your support, but I will get to the bottom of this by having a local professor look at it.

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#32

It’s pretty clear that the tests show that the drops are not independent events between the characters. The drops are either universal with a person’s account or from a global counter.

Since other tests were done between two people with different accounts, it’s pretty clear that a global counter is being used to determine when certain items dropped.

All the jargon in this topic doesn’t really matter. This test, as well as many others in the other DPK topic, basically confirms the existence DPK ^o.o^

#33

I fully agree with this. It is quite rude to barge into this thread from out of nowhere to question/call you out on your methods. If someone on Reddit told him to check this out, he simply could have checked it out and messaged the other person back.

Additionally, it is also rude on the front that he is picking apart your post without offering any specifics on how it could be improved. If a person is going to pop into a thread and say anything, they should at least be fully committed to provide exact information on what needs to be changed. This is mainly why I said, “I wouldn’t worry about it” in my earlier post. And, I have no real idea what his credentials are, I was going on intuition when I was judging his character.

#34

I am still working on this question & I am currently brushing up on sampling techniques. What I learned alerted my BS detector:

@Mathinator claims that I’m comparing Simple Random Samples (SRS). My sampling technique of the wizard (sample set B) and cleric (sample set C), 190 followed by ~8 trials until 1 success, is so painfully obvious not SRS. It is a non random sampling with a specific pattern, as samples obtained in all sample sets do not have the same chance of being selected. In fact, set B is dependent on the last drop, and set C is dependent on 190 trials after the last drop. Furthermore, the samples are collected in categories, which is not what SRS does. Finally, this sampling is closest described as consecutive sampling.

This self proclaimed expert doesn’t even seem to understand the thing he is saying (or the experiment).

This question turned out to be a lot harder than I imagined. Although the conclusion is fairly obvious (DPK), I have yet to find a 3rd party to validate my mathematical proof.

The math professor I sought help will not be back until next week. So answer will come much later.

#35

Very interesting thread. I’m slowly losing motivation from farming reading all these DPK theories. Just the fact we can be farming for hours just for some random person that can KS your work. What items are determine to be DPK and what items are RNG? I’ve read that items on TOSBase with “?” are the DPK but not sure how true that is.

#36

I’ll respond to this for the benefit of the readers.

I’ve not “proclaimed” anything of the sort, but you’ve certainly demonstrated the reverse.

A neat thing you’ll find about mathematicians (if you’re ever around one) is they have no need to proclaim anything - the mathematics speaks for itself…

You’ve gone from someone I thought had perhaps forgotten some basic statistics to one I doubt the claim of having ever taken any statistics, much less that of understanding it.

“I used my wizard to farm 190 (minimum interval during initial testing was 192), then using my cleric to farm until I get the CSB.”

The first is an SRS (the fact you had to resort to “…brushing up…” on some simple concepts like that is troubling vis-a-vis your claims of understanding).

The latter is not: it is inverse sampling (it also goes by other monikers, such as sequential sampling, etc., so your discovery of a what you think is a different name (which, btw, is also not correct terminology for what you’re doing) means nothing, other than you probably didn’t understand it in the first place).

The problem, readers, is the comparing of the two types of samples as if they produce directly comparable results. They don’t (and generating a confidence interval from such a biased sample strains credulity).

For the mathematically inclined (and for the “professor”, wink wink, that’s going to be consulted), it is easy to show via appropriate manipulations of the negative binomial, the OP’s “sample until one success” scheme results in a biased average sample estimator of F(2,1)(1, 1, 1 + s, 1 - p) p, where F(2,1) is the ordinary hypergeometric function (sorry for notation, no math markup here), and s, p are stopping rule and true population parameter.

IOW, sampling this way exaggerates the estimator, and the lower the true probability, the worse the exaggeration.

Fortunately, the simple-minded “stop on first success” used by the OP for the comparison sample results in s=1, which allows simplification of the form to a pretty p Log§/(p-1). You can plug in various true probabilities to see how exaggerated the mean of the sample estimator becomes.

For the not mathematically inclined, a simple experiment will easily demonstrate the flawed thinking at work in the OP:

Say I offer you the following game.

You will take a fair regular (6-side) die, and roll it until you see a one, where you will then note the number of rolls needed and the “drop rate” estimated therein for ones (1/(number of rolls)).

You will repeat this ten times, and average the “drop rate” estimates.

I offer to wager on that mean of estimators being >1/6 (that is, I’m betting the estimator will be biased and exaggerate the true 1/6 population parameter for the one on the die).

Would you play the game?

Now, it should be obvious that were you to do this via SRS (that is, pick a sample size ahead of time, as is done in proper experimental design), on some reasonable sample size (oh, say 30 or so per set), this will result in a binomial distribution of results, whereby it can be easily seen the mean estimator will be 1/6 on average, and the probability of getting a mean over 10 sets of such samples is also binomial, with probability ~0.46. IOW, it’s an unbiased sample technique, and the probability of getting an average of estimators >1/6 is <0.5 (as s/b obvious from the relationship of the Mean and skewness of the distribution).

But, you’re not using an SRS in the game, you’re using the OP’s stop on success rule. If that scheme is unbiased, you’d gladly play - you have a positive expectation (i.e., you’ll win more than lose).

The reality: I’ll win nearly 99% of the time, and the mean of the estimators for the sets will be over double the true rate. (And remember, that bias gets worse as the true probability goes down, so…).

Don’t believe it? Grab a die and do it yourself, or even a coin (though the bias will be lower, since probability is 1/2, but *it will be biased).

I have no doubt the OP will respond with some information from a “professor”. It will either be an admission (since mathematics does not lie, and anyone competent in the field will verify the above), or it will be… welll, me thinks the only “professor” spoken to may be the one on the reruns of Gilligan’s Island on the OP’s TV.

As already said, the conclusion may be correct (by happenstance perhaps), but the techniques demonstrate more and more extreme ignorance of basic statistics.

I’ve provided a couple of test ideas that would not have these issues (cluster analysis or goodness-of-fit to the Pascal distribution), which the OP appears to argue are inferior tests to their flawed tests. Beats me at this point what their motivation is in all this…

I don’t enjoy calling out people, but this BS has gotten out of hand.

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#37

I find it super entertaining to watch people try to figure this out considering some of us already know exactly how it works without all this trouble.

Hell lemme drop my 2 cents about I guess. I will just provide some glaring reasons why the drop rate is NOT a DPK system. Some of this I have mentioned before but let me repeat it and then some.

From the view of a programmer this is simply awful and not because the code would be hard to write but because the code would ultimately be impossible to optimize in a client/server environment. It quickly becomes an issue when every players kills are being tracked in real time and even more so when you start talking about how this interacts across channels. Sure it could be a single channel system though I can’t help but see serious issues if it were. Essentially all the data being tracked and sent/received all the time would really clog things up. And yeah I know the game runs like crap but I don’t think the issue comes from to much data flowing all over like this example here would be.

If there were a DPK system would it actually be reliable or would the system still be random considering you could just float around in heavily farmed areas trying to be the lucky one that kills the final mob needed. This is basically like playing the lottory all the farmers in the channel acting like the people playing tickets for the lotto and someone is gonna hit that lucky number. It would be just that though LUCK. If the drop is not tracked by ONLY your kills then you could do 99.9% of the work for someone else to come in kill one thing and take the loot and leave. If it tracks ALL players kills or just the total number of killed mobs on the map until one mob is the 1000th one needed for a drop to happen then it is STILL LUCK. This essentailly makes the system RNG in practice because no one knows how many were killed or which one currently on the map is going to be that magic number. I guess this doesn’t support DPK or really disprove it much but it is a very important thing to remember.

My own experience having farmed a LOT. The system is total rng. I have farmed the same spots over and over and many times when the area was empty which is super important. During many runs through empty maps I was able to pull super low drops pretty much back to back. However I was also stuck grinding the same map for hours with nothing plenty of times too. The incredible gaps and inconsistencies in drops pretty much disproves the whole DPK system really. If there was a specific number of kills needed to have something drop I of all people would already know this and be abusing it all day every day. I have payed very close attention to the number of kills it has taken me to get rare drops I always have because I like to see what it’s taking me to get things. Huge huge inconsistencies in rates if a DPK system existed but NOT inconsistent with a range based on a % chance.

Hell even the flashing mobs aren’t coded to be DPK. Flashing mobs are STILL a chance drop and not a sure thing. Ever kill a flashing mob that had the lucky buff icon and NOTHING. It even said on one of the loading screens once that the flashing mob “could” drop up to 10x the normal loot. “could” not “will” meaning there is a chance regardless of odds but still a chance.

These are my best arguments. DPK simply doesn’t make sense and there is no evidence supporting it. Not to mention it would make little sense to go through the trouble of creating a system that probably wouldn’t work when every mmo in the history of the genre has used a basic rng system for most loot where the enemy death triggers the generation of loot based on a pool and percentage chance. Yeah there are some exceptions in some games but the exceptions are usually a special set of conditions such as an event where you get items for x kills and turn them in for things. This is usually made clear and is a sub-system and not the main loot method.

So carry on if you want with this unnecessary experiment that may or may not even be accurate or correct. I will sit back and watch because I already know this DPK thing is nothing more than gamblers fallacy.

#38

You said alot and nothing at all.

No you dont.

Mathinator and CookyKim are arguing about the methodology of the tests.

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#39

haha Gotta love those @CookyKim Topics / Debates!

Anyway, this is a very good thread, and it really brings to light that it is quite possibly a DPK Drop system. Along with what @Flash said:

Pretty much confirms that is it is in fact DPK system. I dont think they had enough work force to overhaul the entire drop system since the 1st korean cbt. especially since there are high priority bugs and more ranks to make :slight_smile:

I don’t agree with this. We already know that the server is keeping track of all the players kills on the server via the journal system and ranking system. it would actually be easier for the programmer to use a DPK system that piggy backs on that system. pretty much signaling which kill will drop which item.

Also, i believe that the Server Latenancy ( possibly due to large data payloads per user ) is why most people are experience low FPS in this game. which is why it seems that videos of people playing in Korea have much better FPS then the ones in the International Version.

I kind of agree here. However, the “Luck” factor is altered when the server is heavily populated and there are more and more people killing the same mob ( you would hit the DPK rang faster resulting that the item will drop for more people more often ). However, if you have a low population server, or a low populated map, it will be Extremely hard to get an item you are hunting for.

for example, if Person A is the only person on a map, and kill 900 mobs, Person B can show up on the map and kill one mob and get the item. Person A would have to kill another 900 to get the item again.

HOWEVER, I dont think that a DPK drop system is all that bad.

I think a good idea to fix this would be to have a sliding DPK value that is dependant on the map population ( doesnt have to be real-time population, could be recorded at intervals ) and some sort of slight RNG buffer. That way, its a bit harder to exploit item drops and allows it to be a bit fairer to low populated maps.

#40

Flat out saying DPK doesn’t exist is a pretty outrageous claim. It certainly exists and I have some pretty solid evidence that it does.

Take the infamous white boater hat as an example. If you watch players farm for it, you’ll pretty quickly notice that the white boater simple has never dropped on the day of or after maintenance. Maintenance is usually on Tuesdays. You might say, “Oh that’s just RNG and most people play on weekends anyway.” Yes, that is true. But that doesn’t change the fact that the boater has dropped on every day of the week except Tuesday and only a single one has dropped very early Thursday morning on Klaipeda.

There’s also the situation of hats that exist in the game but no one has ever found them. A good such example of a hat is the Red Ribbon Decoration Boater. In fact, I know exactly where it drops with 99% certainty, but the problem is farming it would take too much time for myself and I cannot devote the time presently to get it. When I have the time I’ll happily farm for it just to prove a point.

Even if an item has a very low drop chance from a rare mob, it will eventually drop as stated by the Law of Large Numbers. The fact many items have not been dropped a single time begs some serious questions if nothing drops.

DPK does not apply to most items or, if it does, it simple uses pseudo rng as opposed to raw rng. DPK most likely is flagged for specific items which a player can simply dub as ‘Ultra-Rare’

I’d like to address one point in particular that stood out to me.

If it tracks ALL players kills or just the total number of killed mobs on the map until one mob is the 1000th one needed for a drop to happen then it is STILL LUCK. This essentailly makes the system RNG in practice because no one knows how many were killed or which one currently on the map is going to be that magic number.

Lets break this down mathematically. Numbers never lie after all.

A hypothetical item running through the DPK system has a preprogrammed drop rate of 1 in 100. After 100 kills in channel X, the item is guaranteed to drop on that 100th (or 101st, interpretation depending) kill. For a fresh channel with reset DPK, the first kill would have a 0% chance of drop, the second kill 0% (infinite significant figures) and so forth up until the deciding kill to which the drop is 100% guaranteed to drop. This is assuming uninterrupted farming with no other players farming in the channel.

Player A, the original player farming for the item, goes AFK after getting some kills denoted as X and a new player, Player B, joins the channel and begins farming. Player B notices Player A and as such, should realize that DPK now needs to be interpreted completely differently. Allow me to explain.

Assuming Player A and Player B do not know each other, Player B would know of the existence of kill count denoted as X, but will not know its actual numerical value. You, sir, claim that this means the system is RNG and this is where you are wrong. The channel in the eyes of Player B is using escalating odds or pseudo RNG. Continuing with the explanation.

Player B begins farming now that Player A is AFK. With each kill, his odds of finding the item increase. After 5 kills, his odds of finding the item are at the bare minimum, 5/100 = 5 Percent. After 20 kills, 20/100 = 20 Percent at the bare minimum and so forth. As he gets more and more kills, his odds of finding this item approach 100% as the number of kills Player B has approach 100 solving for the function (X + n)/100 = % chance where n = the number of kills Player B has. Player B will inevitably find the item when X + n = 100, completing that item’s DPK cycle.

In case you think that normal RNG works similarly, you would be mistaken. If an item has a 1% drop rate, it is NEVER guaranteed to drop after 100 kills. An mob could go 100000000 kills without ever dropping the item or drop it 10000 times in a row. The odds of it happening are low sure, but the Law of Large Numbers will inevitably level the drop rate out to it’s proper 1% through average.

In regards to the whole programming aspect; every mmo worth its salt and if it would ever even hope of working has very detailed logs. MMOs have logs for the express purpose of: catching bots, verifying lost items, verifying and banning for RMT, catching dupers, and detecting suspicious account activity. A number just holding the amount of kills on a channel for any given mob would take up such a minuscule amount of memory, especially since it’s held in the RAM. Evidence that it is in the RAM is that when the server crashes a variety of things are reset, such as boss timers.

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#41

What the ■■■■ are you talking about? All kills are already tracked in the entire game for a variety of reasons, seriously what on earth are you even talking about? Like this is the most blatantly “I know nothing about what I’m saying” post I’ve seen in a while. “It would be impossible to optimize a counter that goes up by 1 when a mob dies”, dude do you have any idea the number of variables being tracked at any given time in an MMO? There are thousands of equations happening every second that are a lot more complex than “add 1”.

#42

Thank for you for your time composing this detailed response. I only wish that this was your initial response to this thread, so that that needless confrontations would not have happened.

The only claims I made were these:

I have consistently insisted that I am not an expert in statistics. I even admitted up front that this may not be the correct way to do this. Perhaps I did not emphasize it sufficiently by providing a personal background paragraph. I’ve only had basic statistics a long time ago in college (note: this is not the equivalent of colleges in US, it is somewhat between high-school and undergrad). Therefore our conflict must have stemmed from the fact that you came in expecting a peer in statistical prowess; which isn’t the case.

Due to courtesy, omission or my lack of comprehension, your initial post in this thread appeared brief and deficient. That got me off on the wrong foot.

But what’s done is done. And I apologize for my part of the problem.

Thank you for the detailed explanation. This is the kind of critique that I was hoping for. I attempted to revise sampling techniques because as you rightly brought up, it seemed problematic. Even as I was going through the material this morning before heading out to work, I saw the inconsistencies in my sampling method. But now with your elaboration, it is even clearer the bias involved by sampling this way. For that I thank you for a well given lesson.

On the matter of this statistics professor, it is currently the end of summer holiday and he is indeed absent until next week. In the mean time I am seeking for advice in Cross Validated, a site of Stack Exchange. I do not know if you already use this site, but if you are willing to, I invite you to answer the questions I have (and will have more no doubt) posted there.

As for cluster analysis or goodness of fit you have suggested, I’ve previously stated that I did not know how to do them. And suggested that you to demonstrate it (with no ill intent).

And since you have declined to offer this to our community, I will have to do further reading over this week as I have never learned neither of those.

Now no need to be sarcastic here, I have met many professors in my life, though not many of them are math / statistics professors. Unfortunately, I chose a path that had nothing to do with math / statistics in my higher level of education. It is one of the things I regret, as you can probably tell I do have an interest in math / statistics; and I did pretty well in math back in high school / college (mostly algebra / calculus etc.).

In closing, I just want to clarify that I REALLY want to get this right. I do not necessarily know the correct way to do it now, but I will keep going until I find a solution. I thank you in advance if you are willing to help me on this journey, or if you choose to decline, I can accept that as well.

#43

Too much math, no more.

#44

I am on SE, but mostly on mathoverflow, but that’s for research level stuff. I’m sure you’ll get good info on Cross Validated - there are some excellent posters tthere (Whuber and John Cook come to mind - both excellent in the field, and from what I’ve seen patient, more than can be said for me).

Be sure to make it clear what your are doing now is comparing a random sample with a sequential one, and others will likely point out the issues and others I might have missed.

As I recommended above (and what my first inclination would be were someone to ask me about a similar test need), I’d test the fit of the distribution.

If it’s “RNG” (IID trials), the distribution of number of trials to success will be geometrically distributed. If it has the counter (or some ~equivalent mechanism like a truncated geometric distribution) with as gross a difference as you propose (>=100 between “drops”), a pretty small sample will suffice to have good power in something like a Chi-Square goodness-of-fit test (e.g., sample until 20 or so “drops”).

So, if you can get on the game (I don’t know the game, obviously), and do your thing while tracking attempts (without external interference - don’t know if that’s a confounding factor here) until you’ve had 20 or so successes, then take the results (numbers of attempts to each success) and test its fit against the null hypothesis’ distribution (the plain-vanilla geometric).

If you get a p-value <=0.05, you’ve got evidence to reject the null (that the mechanism is plain “RNG”).

Be careful with wording - that does not prove it’s not RNG, any more than it would prove it’s not - it’s always possible (at least in real-world tests like this) that you’ve just experienced a fluke (and “proof”, like “confidence” have very distinct meanings different from the colloquial use)… But a low p-value is strong evidence against the null.

If you don’t have it already, you can use, e.g., R to do the test - it’s quite good and free.

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#45

While the sample size isn’t very big. I can see the trend you’re trying to show and to make sense about.

While this shows drop rate may not be “pure” RNG, it does not show that it is DPK. It only shows that there may be correlations between the kill count and the drop chance, and there can be many theories people can think of to explain such drop system.

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#46

The only way to ‘proof’ is to see the source code, or make IMC staff go clear things out. Also notice that there is no real RNG in computers currently.