Explanation
In this experiment, we try to determine which face is going to be shown when we toss
the coin above.
A good number of the things around us occur randomly i.e in an unpredictable manner.
It is difficult to tell when an event will occur but we can determine the likelihood of
the event occuring.
That is what Probability is all about.
Probability refers to the likelihood of an event to occur.
In trying to determing how likely an event is to occur, we carry out trials. Trials because it is a game of chance.
For our trials to be fair enough, they have to be free from systematic errors. In this coin tossing experiment,
the systematic error we try to free ourself from is bias. We want to toss the coin more than twice to have a large space
of outcomes we can infer from.
Our coin tossing experiment is one of the simplest problems in probability which involves just two outcomes and therefore, is binary - face or head.
The number of trials conducted represent the size or sample space of our experiment.
An event, which result from a trial is one of the possible outcomes which possesses unique attributes.
The result of one experiment may differ from that in another experiment and so, the occurrence of an event is random.
In some cases, albeit, it is possible that some patterns repeat themeselves in an experiment when some conditions are similar to some other experiments.
In those special cases, it is possible to predict the outcome of the next experiment.
Take this coin tossing experiment for example:
Each time you toss the coin, the output is recorded in a list of experiments which is used later on.
With this list of outcomes, looking at the number of times a Head occurrs between two Tails and when it does not, aggregated, gives us a frequency of Heads. This applies to Tails too.
This way it is possible to determine what could be the next outcome in our experiment.
In summary, the sum of frequency of occurrence of Heads or Tails to the total number of experiments carried out tells us which one is likely to occur again.
Thank you for reading ;)
All the code for this experiment can be found here.