How to find the sample Size

 


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Precision, Significance Level, Confidence Level, Confidence Interval, Power, Degree of Freedom, p-value,aql, z-value, t-statistics Explained

Precision : In hypothesis testing, precision means how sure we want to be before making a decision. For example, if we want to know if a new medicine is better than an old one, we need to decide how sure we want to be that the new medicine really is better. Do we want to be 90% sure, 95% sure, or 99% sure? The higher the level of precision we choose, the more sure we are before making a decision.  It refers to the level of accuracy or the margin of error that you are willing to accept in your estimate or measurement. For example, if you choose a precision level of 5%, it means that you want to be 95% confident that your estimate is within 5% of the true value. In traditional hypothesis testing using the normal distribution, precision is not explicitly used because the significance level is used instead. The significance level determines the probability of rejecting the null hypothesis when it is actually true. It is often set to 0.05, which means that we are willing to accept a 5%...
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Zero Inflated Poisson Distribution: What it is

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The zero-inflated Poisson distributio n is a mixture of a point mass at zero and a Poisson distribution. It is suitable when there is a significant number of zero values in the dataset, and a few datapoints have large values. This distribution allows for the excess of zeros and captures the occurrence of larger values. Intuition Imagine you have a dataset that represents the number of events that happen within a certain time or space, like the number of cars passing by a particular street in an hour. Usually, many hours will have no cars passing by, and only a few hours will have some cars passing by. The Zero-Inflated Poisson Distribution is a way to describe this kind of dataset. It combines two ideas to better represent the data: Lots of Zeroes : First, it acknowledges that many hours have no cars passing by. These zero counts are more common than any other number of cars. So, it considers two possibilities: either there are no cars at all (zero count) or the usual Poisson distrib...
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