Of the first 10 digits of Pi, only 3 are even numbers. According to theory, even and odd digits of any irrational number, such as Pi, are supposed to be equally probable. Does this little experiment mean that the theory is invalid?

If you try this experiment again (e.g., look at ten digits further along), you will probably get a different result. Moreover, if you examine a sequence with an odd number of digits, then you can **never** get the "correct" answer. What is going on here?

Clearly, there is a **range** of possible results from experiments such as these and you should not take any single result too seriously, even if it is the ML result -- meaning that any other result is less likely.

First of all, the question above does not completely define the goal and there are different kinds of answers. Even more important, it says nothing about whether you have done this experiment before. For instance, if this is a routine task that you have carried out twice a day for the past ten years, then you already know the degree to which this point estimate is representative of the population comprising all such results. This **prior information** would have a very strong influence on the range that you reported. In fact, if you are a real expert, then you could report a valid range without even looking at this sample!

The range described above is called a **confidence interval**.^{1} Most often cited is the **central** confidence interval for which the probability of being wrong is divided equally into a range of proportions below the interval and another range (usually of different size) above the interval. Alternatively, the **shortest** (narrowest) such interval is sometimes desired. In either case, the corresponding **confidence limits** define the boundaries of the interval.

If the stated assumption is true, then the confidence limits computed by this calculator are **exact** (to the precision shown), not an approximation. Some Technical Details are described below.

- # Successes, k (an integer ≥ 0)
- # Examined, N (an integer > 0 and ≥ k)
- Confidence (a.k.a. "degree of belief"), 1 - X, where 0 < X < 1

[default confidence is 0.95 -- i.e., a 95-percent confidence interval]

Calculator:
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Central Confidence Interval:
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Shortest Confidence Interval:
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If you try this with 3-out-of-10, as in the experiment above, you will find that the theoretical answer, 0.5, lies within either of the 95-percent confidence intervals. Hence, you can be "95% sure" that the observed 3/10 does **not** contradict theory. If you need to be only 90% sure, enter 0.9 for the Confidence. Does the theory still hold up? How big must you allow your chance of being wrong, X, in order to assert that these 10 data points contradict theory?

For each confidence limit, this calculator computes the inverse of the appropriate cumulative Beta distribution, with parameters determined by the values that you enter.

The short answer is that the calculation done here is extensible and relevant procedures are described in many textbooks and papers on Bayesian statistics. In general, as your prior information increases, the confidence interval on a new observation gets narrower, *ceteris paribus.*

^{1}Some Bayesian statisticians would prefer the term "credible interval."

^{2}This calculator might fail when k*N is very large.

^{3}See
A Compendium of Common Probability Distributions.

References:

Nicholson, B. J., 1985, "On the F-Distribution for Calculating Bayes Credible Intervals for Fraction Nonconforming",IEEE Transactions on Reliability,vol.R-34(3), pp. 227-228.N.B. The calculator on this webpage does not use the F-distribution; it computes the inverse Beta directly.

Jaynes, E. T., 1976, "Confidence Intervals vs. Bayesian Intervals", in

Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science,W. L. Harper and C. A. Hooker (eds.), D. Reidel, Dordrecht, pg. 175. [8Mb (.pdf) download]