The randomness of the location from where results originate ensured that each candidate’s strongholds got an equal chance of arriving at the tallying centre.

This phenomenon is similar to what happens when one is eating githeri. Suppose you scoop spoonfuls from different parts of the plate. Each spoon will have a different ratio of maize to beans. As you swallow the food, the ratio of maize to beans in your stomach initially varies widely but it quickly settles to a fairly constant value – well; only that now it is chewed! The constant value is approximately equal to that of the seeds in the plate.

In this illustration, the plate is the people who voted, the spoonfuls are the results from the polling stations and you stomach is the tallying centre at Bomas of Kenya.

What about the normal distribution? First of all, it is not the appropriate curve for this situation; anyone expecting to see it here has obviously never studied statistics! The correct graph is known as the Poisson Distribution. It describes situations where the data arrives in discrete packets – for example, the number of votes per candidate coming from discrete polling stations.

 

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