Formal analysis of birth control aggregation

The quantity we are interested in is, for some sample of 100 couples c_{i},i=1\ldots 100,

\mathrm{E} \left[\sum_{i=1}^{100} I(c_i \hbox{ has an unplanned pregnancy over 10 years})\right] ,

where I(\cdot) is an indicator variable. We can exchange the sum and the expectation (expectation is a linear operator) and this does not rely on the independence of the couples or their outcomes:

\sum_{i = 1}^{100} \left[ \mathrm{E}_i I(c_i\hbox{ has an unplanned pregnancy over 10 years})\right].

Note that here the expectation is still with respect to the distribution for couple c_iwhich is why I’ve subscripted it. Now, an expectation of an indicator is just a probability, so we can simplify.

\sum_{i = 1}^{100} \left[ \Pr_s(c_i\hbox{ has an unplanned pregnancy over 10 years})\right].

The next step is where I diverge from the NYT. They (implicitly) assume that \Pr_i(\cdot) = \Pr_1(\cdot) for all i. That allows another simplification:

100\cdot\Pr_{1}(c_{1}\hbox{ has an unplanned pregnancy over 10 years}).

I’m challenging that assumption, since it would be rather odd to sample 100 couples and find they all had identical distributions. Without knowing something of the distributions, you have to leave it at the penultimate step above. In my numerical example, I’m imposing a mixture of two types with distinct distributions, which would still be consistent with the observed group single-year effectiveness, but which results in quite a different answer to the question above.

To actually calculate things we can treat the probability as a single Bernoulli trial lasting 10 years, or we can break it down into a sequence of 1-year Bernoulli trials by imposing a (potentially reasonable) independence assumption. Actually, this doesn’t matter for my argument – the error has already happened by this point.

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