Imagine you have a process that follows a negative binomial distribution: for each trial conducted, an event either occurs or does it does not, referred to as "successes" and "failures". The frequency with which successes occur is variously referred to as the success fraction, success ratio, success percentage, occurrence frequency, or probability of occurrence.

If, by experiment, you want to measure the the best estimate of success fraction is given simply by k / N, for k successes out of N trials.

However our confidence in that estimate will be shaped by how many trials were conducted, and how many successes were observed. The static member functions negative_binomial_distribution<>::find_lower_bound_on_p and negative_binomial_distribution<>::find_upper_bound_on_p allow you to calculate the confidence intervals for your estimate of the success fraction.

The sample program neg_binom_confidence_limits.cpp illustrates their use.

First we need some includes to access the negative binomial distribution (and some basic std output of course).

#include <boost/math/distributions/negative_binomial.hpp>
using boost::math::negative_binomial;

#include <iostream>
using std::cout; using std::endl;
#include <iomanip>
using std::setprecision;
using std::setw; using std::left; using std::fixed; using std::right;

First define a table of significance levels: these are the probabilities that the true occurrence frequency lies outside the calculated interval:

double alpha[] = { 0.5, 0.25, 0.1, 0.05, 0.01, 0.001, 0.0001, 0.00001 };

Confidence value as % is (1 - alpha) * 100, so alpha 0.05 == 95% confidence that the true occurrence frequency lies inside the calculated interval.

We need a function to calculate and print confidence limits for an observed frequency of occurrence that follows a negative binomial distribution.

void confidence_limits_on_frequency(unsigned trials, unsigned successes)
{
   // trials = Total number of trials.
   // successes = Total number of observed successes.
   // failures = trials - successes.
   // success_fraction = successes /trials.
   // Print out general info:
   cout <<
      "______________________________________________\n"
      "2-Sided Confidence Limits For Success Fraction\n"
      "______________________________________________\n\n";
   cout << setprecision(7);
   cout << setw(40) << left << "Number of trials" << " =  " << trials << "\n";
   cout << setw(40) << left << "Number of successes" << " =  " << successes << "\n";
   cout << setw(40) << left << "Number of failures" << " =  " << trials - successes << "\n";
   cout << setw(40) << left << "Observed frequency of occurrence" << " =  " << double(successes) / trials << "\n";

   // Print table header:
   cout << "\n\n"
           "___________________________________________\n"
           "Confidence        Lower          Upper\n"
           " Value (%)        Limit          Limit\n"
           "___________________________________________\n";

And now for the important part - the bounds themselves. For each value of alpha, we call find_lower_bound_on_p and find_upper_bound_on_p to obtain lower and upper bounds respectively. Note that since we are calculating a two-sided interval, we must divide the value of alpha in two. Had we been calculating a single-sided interval, for example: "Calculate a lower bound so that we are P% sure that the true occurrence frequency is greater than some value" then we would not have divided by two.

   // Now print out the upper and lower limits for the alpha table values.
   for(unsigned i = 0; i < sizeof(alpha)/sizeof(alpha[0]); ++i)
   {
      // Confidence value:
      cout << fixed << setprecision(3) << setw(10) << right << 100 * (1-alpha[i]);
      // Calculate bounds:
      double lower = negative_binomial::find_lower_bound_on_p(trials, successes, alpha[i]/2);
      double upper = negative_binomial::find_upper_bound_on_p(trials, successes, alpha[i]/2);
      // Print limits:
      cout << fixed << setprecision(5) << setw(15) << right << lower;
      cout << fixed << setprecision(5) << setw(15) << right << upper << endl;
   }
   cout << endl;
} // void confidence_limits_on_frequency(unsigned trials, unsigned successes)

And then call confidence_limits_on_frequency with increasing numbers of trials, but always the same success fraction 0.1, or 1 in 10.

int main()
{
  confidence_limits_on_frequency(20, 2); // 20 trials, 2 successes, 2 in 20, = 1 in 10 = 0.1 success fraction.
  confidence_limits_on_frequency(200, 20); // More trials, but same 0.1 success fraction.
  confidence_limits_on_frequency(2000, 200); // Many more trials, but same 0.1 success fraction.

  return 0;
} // int main()

Let's see some sample output for a 1 in 10 success ratio, first for a mere 20 trials:

______________________________________________
2-Sided Confidence Limits For Success Fraction
______________________________________________
Number of trials                         =  20
Number of successes                      =  2
Number of failures                       =  18
Observed frequency of occurrence         =  0.1
___________________________________________
Confidence        Lower          Upper
 Value (%)        Limit          Limit
___________________________________________
    50.000        0.04812        0.13554
    75.000        0.03078        0.17727
    90.000        0.01807        0.22637
    95.000        0.01235        0.26028
    99.000        0.00530        0.33111
    99.900        0.00164        0.41802
    99.990        0.00051        0.49202
    99.999        0.00016        0.55574

As you can see, even at the 95% confidence level the bounds (0.012 to 0.26) are really very wide, and very asymmetric about the observed value 0.1.

Compare that with the program output for a mass 2000 trials:

______________________________________________
2-Sided Confidence Limits For Success Fraction
______________________________________________
Number of trials                         =  2000
Number of successes                      =  200
Number of failures                       =  1800
Observed frequency of occurrence         =  0.1
___________________________________________
Confidence        Lower          Upper
 Value (%)        Limit          Limit
___________________________________________
    50.000        0.09536        0.10445
    75.000        0.09228        0.10776
    90.000        0.08916        0.11125
    95.000        0.08720        0.11352
    99.000        0.08344        0.11802
    99.900        0.07921        0.12336
    99.990        0.07577        0.12795
    99.999        0.07282        0.13206

Now even when the confidence level is very high, the limits (at 99.999%, 0.07 to 0.13) are really quite close and nearly symmetric to the observed value of 0.1.