Probability of this facebook likes distribution on my new page I just created a facebook page for residents in greenland and denmark and I'm wondering if the distribution of likes.
The population of greenland is:    56.483
The population of denmark is:   5.614.000

I have the following likes so far:
Greenland: 11
Denmark:   32

It is obviously skewed in some way. If the hypothesis is that people from greenland and denmark would have the same ability and reason to like the page, what is the probability that I get this distribution by chance?
Note: This is not homework I have a facebook page and I'm genuinely interested in why the distribution is so skewed. And even though it isn't homework I'm also interested in how you would calculate this. 
Finally... Sorry if this problem is too simple.
 A: The classical statistics treatment of this problem would be something like this.  
Null hypothesis: every member of the population of Greenland and Denmark, independently of everybody else, has the same unknown probability $p$ of liking your page.  Nobody who is not a resident of Greenland or Denmark can like the page. 
Given a total of $43$ likes for the two pages,  every set of $43$ people from the combined population has the same probability of being the ones who liked the page.  The conditional probability of getting exactly $x$ likes from Greenland is then
$$ p(x) = \dfrac{{56483 \choose x} {5614000 \choose 43-x}}{5614000+56483 \choose 43} $$
The conditional probability of at least $11$ likes from Greenland would be
$$ \sum_{x=11}^{43} p(x) \approx 4.105 \times 10^{-13}$$
This is extremely small, so we should reject the null hypothesis.
A: *

*Populations of small size, very low density and low urbanization may have more reason to depend on Internet for social interaction, than larger populations living at higher density in cities.   

*Social network connections and actions on the network are positively inter-correlated.  Whether somebody will click "like" on a post is correlated with whether their "friends" in the social network liked it.   A lot of things that occur on the network have a much higher probability of happening than in a model where the decisions on "likes" are made independently.
