For example: Person A may choose to stop tossing a coin when the total count reaches 100 while B stops at 1000.For different sample sizes, we get different t-scores and different p-values.
It is the most widely used inferential technique in the statistical world.Prior knowledge of basic probability & statistics is desirable.You should check out this course to get a comprehensive low down on statistics and probability.In several situations, it does not help us solve business problems, even though there is data involved in these problems.To say the least, In 1770s, Thomas Bayes introduced ‘Bayes Theorem’.This experiment presents us with a very common flaw found in frequentist approach i.e.
being applied to numerical models to check whether one sample is different from the other, a parameter is important enough to be kept in the model and variousother manifestations of hypothesis testing.
This makes the stopping potential absolutely absurd since no matter how many persons perform the tests on the same data, the results should be consistent. I) are not probability distributions therefore they do not provide the most probable value for a parameter and the most probable values.
These three reasons are enough to get you going into thinking about the drawbacks of the . From here, we’ll first understand the basics of Bayesian Statistics.
And it's much harder to do with functions, unless you're good with R functional programming, than with discretized values for $p$.
I'm constructing an example similar to what you were doing, but not exactly the same.
Even after centuries later, the importance of ‘Bayesian Statistics’ hasn’t faded away.