Odds Ratios

Binomial $(n, p)$ probabilities involve powers and factorials, both of which are difficult to compute when $n$ is large. This section is about a simplification of the computation of the entire distribution. The result also helps us understand the shape of binomial histograms.

Consecutive Odds Ratios

Fix $n$ and $p$, and let $P(k)$ be the binomial $(n, p)$ probability of $k$. That is, let $P(k)$ be the chance of getting $k$ successes in $n$ independent trials with probability $p$ of success on each trial.

For $k \ge 1$, define the $k$th consecutive odds ratio

To see how this helps us calculate each $P(k)$ without having to calculate factorials and powers each time, notice that

and so on.

How is this more illuminating than plugging into the binomial formula? To see this, fix $k \ge 1$ and calculate the ratio $R(k)$.

First, notice that the formulas for $R(k)$ are simple. For example, if $n \ge 3$, it is easy to calculate $P(3)$ as

No factorials involved.

Shapes of Binomial Histograms

Now observe that comparing $R(k)$ to 1 tells us whether the histogram is going up, staying level, or going down at $k$.

Note also that the form

tells us the the ratios are a decreasing function of $k$. In the formula, $n$ and $p$ are the parameters of the distribution and hence constant. It is $k$ that varies, and $k$ appears in the denominator.

This implies that once $R(k) < 1$ for some $k$, it will remain less than 1 for all larger $k$. In other words, once the histogram starts going down, it will keep going down. It cannot come back up again.

That is why binomial histograms are either non-increasing or non-decreasing, or they go up and come down. But they can’t look like waves on the seashore. They can’t go up, come down, and go up again.

Let’s visualize this for a $n = 23$ and $p = 0.7$, two parameters that have no significance other than being our choice to use in this example.

n = 23
p = 0.7
k = range(n+1)
bin_23_7 = stats.binom.pmf(k, n, p)
bin_dist = Table().values(k).probability(bin_23_7)
Plot(bin_dist)


# It is important to define k as an array here,
# so you can do array operations
# to find all the ratios at once.
k = np.arange(1, n+1, 1)
((n - k + 1)/k)*(p/(1-p))

array([ 53.66666667,  25.66666667,  16.33333333,  11.66666667,
8.86666667,   7.        ,   5.66666667,   4.66666667,
3.88888889,   3.26666667,   2.75757576,   2.33333333,
1.97435897,   1.66666667,   1.4       ,   1.16666667,
0.96078431,   0.77777778,   0.61403509,   0.46666667,
0.33333333,   0.21212121,   0.10144928])


What Python is helpfully telling us is that the invisible bar at 1 is 53.666… times larger than the even more invisible bar at 0. The ratios decrease after that but they are still bigger than 1 through $k = 16$. The histogram rises till it reaches its peak at $k = 16$. You can see that $R(16) = 1.1666 > 1$. Then the ratios drop below one, so the histogram starts going down.

We can solve an inequality to show that the largest $k$ for which $R(k) \ge 1$ is the integer part of $(n+1)p$. In our example, this is $k = 16$ because

(n+1)*p

16.799999999999997


Mode of the Binomial

A mode of a discrete distribution is a possible value that has the highest probability. There may be more than one such value, so there may be more than one mode.

For all $k$ such that $R(k) \ge 1$, we will say that the binomial histogram is either rising or flat at $k$. The largest $k$ for which $R(k) \ge 1$ has to be a mode; for all larger $k$, the histogram will be falling.

Let $q = 1-p$. Every value $k$ for which $R(k) \ge 1$ must satisfy

That is,

which is equivalent to

Therefore the largest $k$ for which $R(k) \le 1$ is the integer part of $(n+1)p$. That’s a mode of the binomial.

Because the odds ratios are non-decreasing in $k$, the only way in which there can be more than one mode is if there is a $k$ such that $R(k) = 1$. In that case, $P(k) = P(k-1)$ and therefore both $k$ and $k-1$ will be modes. To summarize:

The Mode

The mode of the binomial $(n, p)$ distribution is the integer part of $(n+1)p$. If $(n+1)p$ is an integer, then $(n+1)p - 1$ is also a mode.

But in fact, $np$ is a more natural quantity to calculate. For example, if you are counting the number of heads in 100 tosses of a coin, then the distribution is binomial $(100, 1/2)$ and you naturally expect $np = 50$ heads. You don’t want to be worrying about $101 \times (1/2)$.

You don’t have to worry when $n$ is large, because then $np$ and $(n+1)p$ are pretty close. In a later section we will examine a situation in which you can use $np$ to get an approximation to the shape of the binomial distribution when $n$ is large.