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Fair coins tend to land on the same side they started: Evidence from 350,757 flips

František Bartoš, Alexandra Sarafoglou, Henrik R. Godmann

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Amir Sahrani, David Klein Leunk, Pierre Y. Gui, David Voss, Kaleem Ullah, Malte J. Zoubek, Franziska Nippold, Frederik Aust, Felipe F. Vieira, Chris-Gabriel Islam, Anton J. Zoubek, Sara Shabani, Jonas Petter, Ingeborg B. Roos, Adam Finnemann, Aaron B. Lob, Madlen F. Hoffstadt, Jason Nak, Jill de Ron, Koen Derks, Karoline Huth, Sjoerd Terpstra, Thomas Bastelica, Magda Matetovici, Vincent L. Ott, Andreea S. Zetea, Katharina Karnbach, Michelle C. Donzallaz, Arne John, Roy M. Moore, Franziska Assion, Riet van Bork, Theresa E. Leidinger, Xiaochang Zhao, Adrian Karami Motaghi, Ting Pan, Hannah Armstrong, Tianqi Peng, Mara Bialas, Joyce Y. -C. Pang, Bohan Fu, Shujun Yang, Xiaoyi Lin, Dana Sleiffer, Miklos Bognar, Balazs Aczel, Eric-Jan Wagenmakers

2023 · arXiv

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Many people have flipped coins but few have stopped to ponder the statistical and physical intricacies of the process. We collected $350{,}757$ coin flips to test the counterintuitive prediction from a physics model of human coin tossing developed by Diaconis, Holmes, and Montgomery (DHM; 2007). The model asserts that when people flip an ordinary coin, it tends to land on the same side it started -- DHM estimated the probability of a same-side outcome to be about 51\%. Our data lend strong support to this precise prediction: the coins landed on the same side more often than not, $\text{Pr}(\text{same side}) = 0.508$, 95\% credible interval (CI) [$0.506$, $0.509$], $\text{BF}_{\text{same-side bias}} = 2359$. Furthermore, the data revealed considerable between-people variation in the degree of this same-side bias. Our data also confirmed the generic prediction that when people flip an ordinary coin -- with the initial side-up randomly determined -- it is equally likely to land heads or tails: $\text{Pr}(\text{heads}) = 0.500$, 95\% CI [$0.498$, $0.502$], $\text{BF}_{\text{heads-tails bias}} = 0.182$. Furthermore, this lack of heads-tails bias does not appear to vary across coins. Additional analyses revealed that the within-people same-side bias decreased as more coins were flipped, an effect that is consistent with the possibility that practice makes people flip coins in a less wobbly fashion. Our data therefore provide strong evidence that when some (but not all) people flip a fair coin, it tends to land on the same side it started.

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5 selected excerpts · 5 total mentions

westurner
Neutral mention · rule
"51% Dynamical Bias in the Coin Toss [pdf]" (2007) https://news.ycombinator.com/item?id=38725981 "Researchers flip coins 350k times to find out if odds are 50/50" (2023) https://news.ycombinator.com/item?id=37958554 : "Fair coins tend to land on the same side they started: Evidence from 350,757 flips" (2023) https://arxiv.org/abs/2310.04153
GuB-42
Neutral mention · rule
Just for curiosity sake, I looked at the standard RNG in C# and it is indeed flawed, but probably not enough to be seen unless you are doing formal tests. In [1] it says that numbers have a 0.5034 probability of being odd due to rounding errors in the algorithm that picks a number in a range, which is unacceptable for a simulation, but may actually be better than a real coin toss [2]. The raw RNG is also flawed but not that badly [3] [1] https://fuglede.dk/en/blog/bias-in-net-rng/ [2] https://arxiv.org/abs/2310.04153 [3] https://gist.github.com/fuglede/772402ecc3997ada82a03ce65361...
fbartos
Neutral mention · rule
About a year ago, we embarked on a quest to answer one of the most intriguing questions: If you flip a fair coin and catch it in hand, what's the probability it lands on the same side it started? Today, we are finally ready to share the results. Thanks to my friends, collaborators, and even strangers from the internet, we collected flippin 350,757 coin flips. We ran several "Coin Tossing Marathons" (e.g., https://youtu.be/3xNg51mv-fk?si=o2E3hKa-ReXodOmc) and spent countless hours flipping coins. In short, we found overwhelming evidence for a "same-side" bias predicted by Diaconis, Holmes, and Montgomery 2007: If you start heads-up, the coin is more likely to land heads-up…
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and vice versa. How large is the bias? In our sample, the mean estimate is 50.8%, CI [50.6%, 50.9%]. We also found considerable variance in the same-side bias between our 48 tossers. The bias varied with a standard deviation of 1.6%, CI [1.2%, 2.0%], in our sample. The variation could be explained by a different degree of "wobbliness" between our tossers. If you bet a dollar on the outcome of a coin toss 1000 times, knowing the starting position of the coin toss would earn you 19$ on average. This is more than the casino advantage for 6-deck blackjack against an optimal player (5$) but less than that for single-zero roulette (27$). The manuscript is at arXiv: https://arxiv.org/abs/2310.04153 And the open data, code, and video recordings at OSF: https://osf.io/pxu6r/. Diaconis, P., Holmes, S., & Montgomery, R. (2007). Dynamical bias in the coin toss. SIAM Review, 49(2), 211-235. https://doi.org/10.1137/S0036144504446436

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