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There are 100 people in a room. What are the odds at least 1 person is born in January?

What is the best way to calculate this?

I used Binomial Distribution.

$E(x) = np = 100 \frac{1}{12} = 8.3$

$std.dev. = \sqrt{npq} = 2.76$

$Z(\text{1 person}) = (1 - 8.3) / 2.76 = -2.64$

$p = P(Z<-2.64) = .004$ or $.4%$ odds that less than 1 person is born in Jan

Can a confidence interval calc apply here? If so, how?

(With 95% certainty, the number of people born in Jan is b/w x & y)

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  • $\begingroup$ You're assuming independence and a probability of 1/12 of being born in January. Neither are quite correct (e.g twins, triplets etc exist and have a tendency to be found together after birth, and the proportion of births that are in January isn't actually 1/12, though it's pretty close). $\endgroup$ – Glen_b -Reinstate Monica Sep 15 '10 at 3:24
  • $\begingroup$ @stat-teacher LaTeX works here. Just enclose formulas in dollar signs $...$ $\endgroup$ – Jeromy Anglim Sep 15 '10 at 7:08
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Do you mean odds or probability?

p(at least 1 person born in January) = 1 - p(no one born in January)

Assuming Independence:

p(no one born in January) = P(person 1 not born in Jan)
                            *P(person 2 not born in Jan)
                            *...P(Person 100 not born in Jan)

Assume P(not born in Jan) = (365-31)/365

p(no one born in January) = (334/365)^100 = 0.00013975

So, p(at least 1 person born in January) = 0.99986025

This is a binomial calculation.

A few points.
1) Your question is about probability it is not about statistics, so it is not meaningful to ask for a confidence interval on a probability calculation. A confidence interval is a measure of uncertainty about a parameter estimate. By framing the question as you have you enable a direct calculation of the parameter value (p = 334/365).
2) Not withstanding comment (1) you have attempted to use a normal approximation to the binomial distribution for your calculation. This will produce a serious error when p is close to 0 or 1 (as here) and is not necessary as exact binomial solutions are trivial in this case.
3) Your calculation of the mean or expected value (and Std. dev.) of the binomial distribution is correct. We can use this and the binomial distribution to estimate the probability of seeing a range of results. I will use p(born Jan) = 31/365 ~ 0.085.

We expect to see 8.5 people born in January in a class of 100 people. Of course this is impossible. But the probability of seeing 9 people born in January is:

$$\begin{align} p(K = k|n = 100)&= \binom{n}{k} * p^k*(1-p)^{(n-k)} \\\\ &= \binom{100}{9} * (0.085)^9(1 - 0.085)^{(100-9)} \\\\ &= 1.902*10^{12}*(2.316*10^{-10})*(0.00030855) \\\\ &= 0.13591726 \\\\ \end{align}$$

Where $\binom{n}{k}$ is the binomial coefficient. (Can someone direct me to how I do maths formulas?)

Similarly the probability of seeing 8 people born in January is: 0.14315621. These two outcomes cover nearly 28% of the probability.

We can construct an approximately symmetrical bound on the mean of 0.085 by using the cumulative binomial distribution functions. I'll use one in Stata, but I think Excel has them. It turns out the probability of seeing 3 or fewer January birthdays is 2.53% and the probability of seeing 15 or more January birthdays is 2.19%. So the probability of seeing 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or 14 birthdays is 95.3% which is a reasonable estimate of a 95% "coverage probability", although coverage probability has a slightly different meaning in statistics.

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    $\begingroup$ For the formulas, you can get a jump start at meta.math.stackexchange.com/questions/480/… . $\endgroup$ – whuber♦ Sep 15 '10 at 2:34
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    $\begingroup$ Should be 365.25 (roughly) instead of 365 if you want to count leap babies! not that the answer will change very much... $\endgroup$ – Jeff Sep 9 '11 at 1:53
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Just to add few comments to Thylacoleo excellent answer. From the CDC’s National Vital Statistics System recent report, we can estimate the probability of being born on January 1st with a bit more precision.

Month

From page 50 of the report, we the probability of being born in a certain month:

#R code
#Month totals - page 50
m_totals = c(329803, 307248, 336920, 330106, 346754, 337425, 
             364226, 360103, 359644, 354048, 320094, 343579)

m_probs = m_totals/sum(m_totals)

This gives the probability of being born in January as 0.0806 which is a bit lower that 31/365=0.085. For info, Feb and Nov have probabilities around 0.076(ish).

Day of the week

For fun, (and since it was on the next page), from page 51 of the report we get the probability of being born on a certain day:

#Starting on a Sunday - page 51
> d_totals = c(7563, 11733, 13001, 12598, 12514, 12396, 8605)
> d_probs = d_totals/sum(d_totals)
> round(d_probs,4)
[1] 0.0965 0.1496 0.1658 0.1607 0.1596 0.1581 0.1097

Looks like Sunday is a popular day!

Notes

  • This page pointed me in the direction of the CDC report.
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  • $\begingroup$ Great point! Note, however, that this report would only be directly relevant for a room full of six and seven year olds ;-). Seriously, if you are concerned about the statistics of the question (which I think is great), then it gets interesting once you realize people control birth month and even day of the week (doctors induce labor, etc.) and that over time the degree of control as well as what is favored can change. But please don't take these remarks as quibbles: I'm only pointing out some intriguing consequences of the path you have indicated here. $\endgroup$ – whuber♦ Sep 15 '10 at 14:47
  • $\begingroup$ @whuber I agree with you, my post was rather simplistic (I just found it interesting!) Two other points of interest. First, in the report it gives different birth rates classified by race. Secondly, I'm a bit confused why the Monday birth rate isn't much higher (since the rate is low over the weekend). Instead, it seems Tuesday is the day of choice. $\endgroup$ – csgillespie Sep 15 '10 at 15:54
  • $\begingroup$ would you (as a hypothetical obstetrician) like to come in on Monday mornings to deliver babies, lol? $\endgroup$ – whuber♦ Sep 15 '10 at 18:14
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I will shamelessly propose using simulation (in R) to answer your questions.

rbinom(n = 100000, size = 100, prob = 1/12) draws 100 trials from the binomial distribution 100,000 times and returns a vector of the number of 'successes' (i.e., January births, using the naive 1/12 probability).

You should be able to approximate whatever you want from the results.

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