Assume that we flip a coin that has a probability of $p > 0.5$ to come up heads.
If we flip the coin $n$ times, we can define the binomial tail as the probability of observing less than or equal to $k$ heads. Let's define that as: $$P(n,k,p) = \sum_{i = 0}^k {n \choose i} p^i (1-p)^{n-i}$$
Is it true that $P(n,\frac{n}{2},p) > P(n+1,\frac{n+1}{2},p)$ for $p > 0.5$? Or with other words, does the probability of observing more tails than heads decrease if we flip the coin more often?
Intuitively, this should be true, since the more we flip the coin, the more representative the sample must be. Thus, observing less heads than tails should become more unlikely, since $p>0.5$. But I can't see how to prove this.
Note that there are some issues with rounding with this question, which I'm not super sure how to resolve elegantly. Any suggestions are most welcome.
R, for instance. Here's a plot of the first 30 values (for a fixed $p$):P <- function(n, p) pbinom(n/2, n, p); plot(P(1:30, 1/2+1e-6))– whuber Feb 20 '22 at 19:38