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first, a '' is literally fair. second, if there is in fact no meaningful difference between the firms then asking for more paperwork is just a wasteful exercise in scrounging for a justification. just more dissipation of value in rent seeking.
@toddjdavey @danielcherny he gets paid out doesn’t he?
@cubeapril really depends on your definition of random. i think a lot of commenters are assuming the idea of a "fair" die into their definition of randomness--and if you're looking for a "fair" coin or d6, the computer will always win over something manufactured.
@polyint @keendawg if someone offered you a price of $0.45 on the outcome of a hypothetical flip while others offered you as much exposure as you wanted at $0.25, would you buy at $0.45 just to back up your contention that a coin flip is worth $0.50?
1. tossing a . this is the simplest possible example. there are two possible outcomes, both having the same probability. https://t.co/acj3u8twja
the expected value of heads in a toss is 0.5. it doesn't mean i'm better at flipping coins if i flip a coin with expected value of 1.0, it means the coin had two sides that were heads, which can be loosely interpreted as "the opportunity was easier".
@mathclubpodcast @mathforlove bonus: using a , design a game with a maximum number of "turns" that you have a one in three chance of winning. :)
@mick0b @streaker_585 @freodockers suspect 1 year contracts at instead of high paying contracts in recent times - similar to what we did with mundy. him and walters are 32yo next year.
@mathforlove @mathclubpodcast i like your process of thinking. but the flip will be random and should not be any different than if you are bifurcating your thoughts based on what to choose based any number of external variables. randomness is conserved in other words.
@mathclubpodcast @mathforlove if the player flips heads they have to choose rock. if a player flips a tails, they get to pick either scissors or paper (exclusive or). this projects the flip of one onto the game of rock, paper, scissors which has a 1/3 probability of winning.
@mathclubpodcast @mathforlove just a thought… suppose we make rock, paper, scissors a game with choice of rock, paper, or scissors for one player decided by a toss of a . the one player cannot see their opponent until the selection is made. the rules…1/2
on our last episode @mathforlove left us with this puzzle: using a , design a game that you have a one in three chance of winning. think you have an answer? let us know... the first person to provide a valid solution will get a shout out on our next episode!
@annatated i am pro masking + air quality improvements but this framing makes me trust the chart less, since i know so many people who have gone to shows and not gotten covid sure i guess it's possible to flip a and get heads like 30 times in a row but it's pretty unlikely
@mgaforlife @youtwitface @cernovich say we play a game with 7 other people (9 total), we each flip a until one of us gets 4 heads in a row. i get 4 heads by the 5th flip. by the 7th flip, i'm still the only "winner". you accuse me of cheating because "no other player got 4 heads in a row"
@mrjacklevin the amount of hate your getting is disgusting, your words are twisted, and how hexicans are turning on you is shameful. you seem like a good person and there are alot of people who appreciate your perspective. the launch is a good experiment.
there is a game involving a being flipped until head appears. if head appears on a throw with an odd index (1st throw, 3rd throw, 5th throw, etc.) the player wins 1 usd otherwise it loses 1 usd. what is the expected payoff when playing this game?
"there are no business problems, there are only people problems." because the complexity of how humans function cannot be fit into neatly designed models that help predict what’s next as reliably as a toss.
@ukaay_udo if you ask a group of good and bad people to toss a . you'll find out the chance of each person getting a head or tail is equal. there's no reason to why bad things happen to good people. but bad things happen to everyone, so does good things 😀
entropy is fairness. the unbiased state. a weighted coin is lower in entropy, and a is the highest possible. engineered systems *cannot* be too high entropy because they actually have to do something predictable
well its always a friday that sends the market into a stir. its do i exit before the weekend or has the selling finished. we have a buy on our strategy for $spy $iwm . sure i'm only partially as you never can predict if its going up or down with a .
@luke_mcshane @mhdksafa let’s say you have a . you bet 5$ that it will come up heads and if it does you get $10 a $5 profit. so your expected return is $2.50. now what if there was a tax so if you won the bet you’d make $7.50 and not $10. are you trying to say you’d now increase your bet?
in a coin flip game, you flip a until the difference between the number of heads and number of tails is 4. you are paid $10 at the end, but you have to pay $1 for each flip of the coins. usen =100000 simulations and find the expected amount https://t.co/f60w9unmie
@johnallenpaulos (assuming a and independent flip mechanics, yadda yadda yadda...)
book fair & coin war results! - https://t.co/armwvvzcmo
@ch_nira @sophiebays @imamaths @beamathsteacher best way to settle is to agree that both are equally likely at any given time. so neither person is more than the other. now send me a so i toss & determine who is the winner of "most" for today.
i just answered today's machine learning question on @0xbnomial! "tossing a " so far, people have answered with 80% accuracy. give it a try here: https://t.co/beumbbvdei
i just answered today's machine learning question on @0xbnomial and got it right! "tossing a " so far, people have answered with 80% accuracy. give it a try here: https://t.co/gswykob6dz
i just answered today's machine learning question on @0xbnomial! "tossing a " #machinelearning #artificialintelligence #probability #mathematics so far, people have answered with 80% accuracy. give it a try here: https://t.co/xpchl0h4fi
today's machine learning question: "tossing a ." you can give it a try here ↓ https://t.co/uqc4zheqe5
problem: - a is tossed repeatedly. show that, with probability one, a head turns up sooner or later. - show similarly that any given finite sequence of heads and tails occurs eventually with probability one. #probability #statistics #problemsolving
i just answered today's machine learning question on @0xbnomial! "tossing a " so far, people have answered with 82% accuracy. give it a try here: https://t.co/ca3o2ncnuk
suppose the experiment e consists of tossing a twice the sample space https://t.co/kfzyvn9qp1
⁦@royalstatsoc⁩ report shows 101 mps asked what is probability of two heads in tossing a twice. half got it right. perhaps some training would be worthwhile. #rsradicaluncertainty https://t.co/htvuyhga3e
@lltemaall and un 🪙😥
a program that runs von neumann's trick for using an un to simulate a returns the exact distribution of heads=1/2, tails=1/2.
@disclosetv looked like fraud, numbers evolved suspiciously stable from 1% to 33% to 50% etc of urns opened; even in tosses, percentages of heads/tails change a lot back and forth until the sample is big enough
@carmacasto @atheistintelli2 this analogy still leaves me confused. the other team likely also trained all season, so your odds are likely a toss-up. if i have faith my will come up heads, is that logical?
fact: the automobile brake was not allowed to look directly at the request of a flip is 75/25 for heads or tails.
1/decentralization: -initial token distribution: #tezos had with 80% going to the public a distribution from the very beginning.
efficiency demands a physical calculation per flip (manipulation is possible by even humans, drawing fire to the idea of a prior to convergence) as a superior method. aggregate collapse to binary output is undefined. all that is left is degree of belief
understanding backwards compatibility is incredibly simple with “basic” probability. flip a “fair” coin, and it converges to 1/2 each side as flips approach infinity. yet each flip is certainly not 1/2 heads or 1/2 tails. nor is the predictive ability relevant for any given flip
@cosmic_horizons @lukebarnesastro @wkcosmo misunderstood - yes. falsification requires at least two competing good explanations - experiment *decides between them*. absent that, an experimental result might be simply “a problem”. that “it came up 100 times” is a theory too. that it is also “a ” is *a problem*.
suppose our experiment is to toss a independently and identically (that is, the same coin is tossed in essentially the same manner independent of the other tosses in each trial) as often as necessary until we have a head, h. let the random… https://t.co/mztoq6gg5n
consider the toss experiment with ω = {h,t} and p(h) = p(t) = 1/2. https://t.co/scdd2mqsmy
@washburnealex @1point618boy flip a coin one time and prove it is a , alex.
the traditional view of p comes from symmetry as found in games of chance & early religious practices. it seems obvious that tossing a yields an equal chance for both sides. you then extend this to more complicated games of chance & so on. 2/x
@vanillatary a part for the obvious bullshit behind it, even the rhetoric does not stand on its own merit. like, you can perfectly say "no, this streak of 127 tails with a does not disprove the binomial formula. this series of 100 heads and 100 tails confirms it"
@lukebarnesastro @wkcosmo no, since such an outcome is compatible with the hypothesis. of course, it is just as probably as any other sequence. in practice, we should conclude that it is likely that the coin is not fair. the question is not “given a , how likely is it that i see 1/2
2) you flip a until you flip three consecutive heads. in expectation, how many flips do you perform? this question can be solved via high school algebra but it takes a bit of cleverness/creativity to see how. can also be monte carlo'd.
great article by dr michaela kiernan and @baiocchimike on statistical power: " the chance of detecting most effects reported in randomized controlled trials in medicine and other disciplines is currently lower than winning a toss of a "
@grpwins don't know what kind of math you use. if you flip a 16 times, you will get 9 or more heads a little over 40% of the time.
@johndelaney if we tossed a in the air 20 times once a day, the chance in any given day of a run of 20 straight "heads" is 1 in 1,048,576. but we know this: one day, it will happen.
if you flip a truly it gives you heads or tails randomly. tampering with the coin to make it heads 90% of the time means 1/10 vs 9/10. we're still measuring 1 flip, so the fractions add to a whole. but the ratio of their probabilities now divides as 1/9 instead of 1/1
@muskuniversity @elonmusk @chainlink the smart-contract of the busyness pay ment etc its up to each part to design agree but payment should be in the not in other form of hack scam able system.
@janedoe10227 @ultralandlord you clearly don't understand what does randomness mean. you could toss a 100 times and get 100 heads in a row. does it look random? no. but that's the nature of randomness.
#poll if these were options, what would you chose? a. receive rs. 1 crore immediately. b. 50% chance of receiving rs. 50 crore (decided thru toss.) vote & reply in comments with reason, if possible.
trading question - 3 coins in a bag, 1 fair ( heads & tails) and 2 unfair (heads on both side). you take a coin and flip it 3 times in order, are htt, what is the probability that the coin that i picked was the ? #sig https://t.co/bxzd3sadty
@hotkartoffel1 to be fair, coin bureau does a very good job explaining the basics.
@evan_van_ness it's good for ensuring a distribution, maturing our energy infrastructure via capturing excess stranded energy and curtailing carbon emissions (and other types of waste) i.e. providing an "energy buyer of last resort", and avoiding centralized/permissioned control.
@insidethepylons or the probability of -flip?
@andrew94315 @frank12271 @ethicalskeptic yes. and anything more than 2 or 3 standard deviations is pretty rare. 5 standard deviations means bizarre as hell. it's as rare as flipping a 20 times in a row and getting tails every time.
@bmwiernik @schotz @fredoswald so, if the alternative is that all probabilities are equally likely, the probability of 4 of 7 is 1/7 or .143. the probability of 4 of 7 under the hypothesis is .273. given the data, the hypothesis is about twice as likely as the uniform alternative.
you take what you believe to be a and flip it three times. it comes up heads every time. how should you update your priors about whether the coin is fair?
@fredoswald fred, but that would be one of the most common misinterpretations of a p-value! https://t.co/1fvcljwqq2 surely, we have moved beyond this, and we know the correct conclusion is 'we can not reject the hypothesis this is a . it might be fair, or not."
you flipped a coin: 4 heads and 3 tails. observation: "hey, this coin is unfair!" (57% heads) estimation: "hey, we need more coin flips!" (95%ci: 18% to 90%) nhst: "hey, this might be a !" (p > .05) https://t.co/8m3zy6kgsk
if kurst coeff is not = 0,5 => markets have memory (the opposite of a ) then arise the necessity for a "memory process" i.e, auto regressive process. an autoregressive (ar) process, remembers where it was. armovingaverage or arma, also arima, are key to describe markets
@tbonier @unitedgeorgia that is not what happened. they weighted their data to account for this. gender composition is roughly flipping a coin. if i flip a a 100 times and you also do that i might get 52 heads but you might get 48 heads. this is noise. anytime you take a sample the demos vary.
so this is just calling a spade a spade… there is a significant amount of randomness in grant selection ( especially near the cut) so might as well toss a ( fair) coin… still, i find it deeply unsatisfactory somehow…
if you used a coin, do this binary thing, i guess? answer to how can i simulate a die given a ? by david thompson https://t.co/wsxoxpayzu https://t.co/wsxoxpayzu
@gitgudkhed i'm done with algo (not even deserving a hashtag). reputation and distribution is central to me. you lost the small respect i started to have for you algo. #ergo is the way : https://t.co/8mogsbxru4
the idea that trends simply carry over by proximity is quite stubborn. what sort of error is it to use limits to 1/2 to describe “ flips” in single instances
@_mbdr_ you are starting to sound more like xi jinping ... zero-covid or bust. the way things work at large scale and the way they work for individuals differ. a fair-coin toss yields 3 heads then it is highly likely that i will get a tail next, but the probability is still 1/2 mem-less
i wasn’t a complete fool, i understood probability enough to know that there is a chance, however small, of a truly behaving in this way. that each new toss had a 50% chance of coming up heads. but it never did. i started to become superstitious and worried.
faster program for a050231: a(n) is the number of n-tosses having a run of 3 or more heads for a (i.e., probability is...
@onejasonknight assuming a , but you didn't say if it was a "fair flip". 😏
chatting to the kids about probability over breakfast assuming a , if you've had 6 tails in a row, what's the chance of flipping a tail with your next flip?
toss a 100x. h, you earn $60; t, you lose $20. in the short term, anything can happen... you could lose more $ than you win or win more $ than you lose. but keep tossing and you're almost certain to win more $ than you lose. that's what trading is: a numbers game.
@pinebrookcap imo you are lucky more than often, it's not explained by my .
@adam3us pow serves multiple purposes in #bitcoin: 1.) issuance (no free lunch) 2.) block rate limitation (physical dos prevention) 3.) byzantine consenus (cost prevents asymmetric lies)
@kaschuta no, it is one of the least probable outcomes. a completely probably won’t give equal head-tail outcomes. it deviates in absolute sense on average by the square root of n. two equally good tennis players almost certainly won’t have equal wins
@bowtiedbettor 0.5 assuming a , you observing the result doesn't matter
@mayfiema @duty2warn 2/ let’s use a known experiment where we have 100% confidence in the evidence: the toss of a . the probability (p) that you get a heads is 50% or p=0.5 so, the p you get a heads is 0.5 or 50%. what is the p you get two heads in a row? to calculate this you multiply:
(far) went up 8.2 percent in the last 1 minutes on https://t.co/rgeav9gixu. #far #crypto #bitcoin #cryptocurrency #btc #ethereum #blockchain #eth #trading #altcoin #binance #cryptonews
@mikeharrisny @nntaleb @r_thaler if casino and you bet with a , how can both lose in the long run?
@terrain222 @enichan you could do even better: flip a and reduce the size in 2 and repeat until you get heads, this way you can save 50% on average!!!
@_julesh_ no problem. just modify your model to account for the fact that it most likely is a .
@1_gutmensch @noamchompers @matthewadelste1 he's doing it that way? well, process reliability explains probability. just as a has probability 0.5 of landing heads, a process with truth ratio 0.99 has probability 0.99 of generating true belief that p. process reliability explains epistemic probability.
toss a 10 times using your left hand. you end up with 3 heads. now repeat this with your right hand and the same coin. suppose you end up with 6 heads. is your right hand twice as efficacious at tossing heads?
@_julesh_ isn’t your “probably have a ” prediction derived from knowledge you have about the world external to the flip test? such as: most coins are produced standard. massive amounts of coins are in production. the relative abundance of rigged coins is low. etc.
idle thought: if you have a coin of unknown bias and you flip it 100 times and you get 53 heads, maximum likelihood estimation says your best estimate of the bias is exactly 53/100, but that would be wrong because you actually probably have a
@carolvdmeijden @hallamartin if we toss a two times, it is possible to assign a head to the treatment group and a tail to the control group. do we expect the treatment group to consist of 1000 heads and the control group 1000 tails?
if a is tossed five times what is the probability that the number of times you observe h is a prime https://t.co/netymsx6sk
@samdgreat01 using tossing of a as an example, the 1st toss can give a head while another give a tail... same process different result👌👌👌. every repeated process are subject to different result...
@coinesper real talk tho, why go to $etc, $erg, or $flux if they could just pull the same #pos'hit you're fleeing from? wouldn't going to a free and like #ravencoin, make sense? or do #eth miners have this happen on their way out? https://t.co/f1ehu3zvpp
what is the #excel formula to work out, say, the probability of tossing h with a at least 4 times in 10 tosses? in #rstats it is pbinom(q = 3, size = 10, prob = 1/2, lower.tail = false). i want a single-cell formula in excel presumably using one of the binom funcs
guesty update soon: decided to convert the upcoming gem crates to gem/coin crates, what do you think is a price for godlies? also a sneak peek into one of our upcoming skins "nathan", holds all 4 of your godlies in each hand... https://t.co/jgx6j4n7be
the probability of flipping a and getting 100 heads in a row is 1 in 2^100. that's 1 in 1,267,650,600,228,229,401,496,703,205,376.
i never said it was a . could be 50%, could be 90%. the likelihood is a function: https://t.co/onbinimbkr
@adrianoferia @peterszilagyi01 “your” new consensus mechanism (proof of stake) has no track record protecting against censorship, yet proof of work is how eth bootstrapped security, and how it achieved (partially) distribution … so it’s, um, not too thoughtful of you to call proof of work obsolete
every day i wake up, it's a coin toss in my brain to see if i hate myself today or not. the catch? it's not a . more often than not mind plays tricks on me...and wins.