Testing the fairness of a coin.
by Andreas Baumann
One of the examples most oftenly used in the introduction of probability is the toss of a coin: we toss a coin, say, 1000 times, and then calculate the frequency of one outcome, such as heads. We then define a fair coin as having . Now we often perform certain procedures to “test” whether the coin is fair – technically, we compute the probability of the observed proportion under the assumed model (“the fair coin”).
However, consider a coin that produces the following results: in odd throws, it will come up heads, and in even throws, it will come up tails. With this coin, we will have a long-run frequency of heads of one-half. So, we find no reason to reject the null hypothesis of “fair coin”.
However, clearly this coin doesn’t produce random results. While it is a fair coin with regards to the proportion of heads or tails, it displays strong auto-correlation. This means that, given the results of throw n, we are very confident in predicting throw n+1. This means that we should take one more aspect into consideration when determining whether or not a coin is fair: independence of the throws.
Now, consider series of 8 throws (I guess a mathematician would call them 8-tuples). What about finding the series “HHHTHT” in these 8-tuples? Clearly, there are possible 8-tuples, of which the following contain the series:
There are 8-tuples of type 1), 4 of the second and 4 of the third – i.e., 12 8-tuples containing the series. We should expect that around 4,7% of the 8-tuples contain this series. However, it is possible to produce a number of 8-tuples (an outcome space) that makes the coin fair according to the standards above, even though it contains a much greater number of this series than one would expect – which leads me to my point:
We often call statistics the science of uncertainty (or randomness); but randomness is not a concept that is unique to statistics. Consider the information theoretical approaches to uncertainty: the two rules above for a fair coin is equivalent to maximizing the Shannon entropy over the parameter space . When moving ahead in statistics, we should always ponder what we really mean by “the null model”.