Say you toss a coin and see tails, and repeat the toss. How many consecutive tails would you need to establish that a coin is biased ($P_{heads} < 0.5$)

Null: $P_{heads} = p = 0.5$

So need $(1-p)^n < 0.05$ to establish bias at 95% confidence one sided

$=> 0.5^n < 0.05$

$=> n > \frac{log(0.05)} {log(0.5)}$

$=> n >= 5$

So it seems like we only need 5 tails to conclude that coin is biased?? That seems very low. Is there something I'm missing?



Yes. However, you'd have to be really careful how you used this criterion of getting five Tails in a row to declare a coin as biased. (More-straightforward tests look at the overall balance between Heads and Tails.)

In the experiment below, I simulated 1000 tosses of a fair coin. The longest run of Tails in those 1000 tosses was of length 8. The R procedure rle counts runs of Heads (1's) and Tailz (0's).

x = rbinom(1000, 1, .6)
Run Length Encoding
  lengths: int [1:511] 1 5 2 1 1 1 2 1 1 1 ...
  values : int [1:511] 1 0 1 0 1 0 1 0 1 0 ...
[1] 8

This same sequence of 1000 tosses happened to have a run of 10 Heads.

[1] 10
  • $\begingroup$ Good point, however, I was mainly implying that you tossed n times, and only observed tails- not that you observed n consecutive tails in a longer chain of tosses. Sorry for the confusion. $\endgroup$
    – dayum
    May 23 at 1:59
  • $\begingroup$ I guessed that's want you intended. But it might be a slippery slope. What if you got a run of six Tails after seeing just one Head? $\endgroup$
    – BruceET
    May 23 at 2:10

Rather than counting tails, I would prefer to calculate a minimum sample size to conclude whether the coin may be biased. Here is one way this can be done, using Chebyshev's inequality. The following formula uses this inequality to get a sample size needed to estimate a mean that is within $\epsilon$ of the true one with probability $1-\delta$, given that we have an upper bound on the variance $\sigma^2$: $$ceil(\frac{\sigma^2}{\delta\epsilon^2}).$$

In this case, the variance is bounded above by 1/4, which is the maximum variance a Bernoulli random variable can have, namely the variance for a fair coin ($(1/2)(1-1/2)$).

For example, we choose $\epsilon=1/10$ and $\delta=1/20$ (so we have a 95% confidence interval that the true probability is between 4/10 and 6/10), then we should flip the coin at least—$$t=ceil(\frac{\frac{1}{4}^2}{\frac{1}{20}*\frac{1}{10}^2}) = 125$$ times, count the number of heads, and divide by the number of flips to get the estimated probability of heads, which will be within 1/10 of the true probability at a 95% chance.

Another way is Hoeffding's inequality. Using the same notation as before, the sample size for random variables in [0, 1] (including Bernoulli random variables) is— $$ceil(\frac{\ln(2/\delta)}{2\epsilon^2}), $$ so that the same example becomes— $$ceil(\frac{\ln(2/\frac{1}{20})}{2\frac{1}{10}^2}) = 185.$$

See also the reference below.


  • Jiang, L., Hickernell, F.J., "Guaranteed Monte Carlo Methods for Bernoulli Random Variables", arXiv:1411.1151 [math.NA], 2014.

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