Day 7 - Central Limit theorem

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     By Jerin Lalichan 


Central Limit Theorem


    The central limit theorem states that the distribution of means of samples taken from a large population, with replacement, approaches a Normal distribution, even if the population is not normally distributed.




    As we increase the number of samples etc., the graph of the sample means will approach a normal distribution. The sample size must be 30 or higher for the central limit theorem to stand.

    One of the most important feature of the theorem is that the mean of the sample will be the mean of the entire population itself. If we calculate the mean of multiple samples of the population, add them up, and find their average, the result will be the estimate of the population mean.

    The same applies when using standard deviation. If you calculate the standard deviation of all the samples in the population, add them up, and find the average, the result will be the standard deviation of the entire population.


   
  I am doing a challenge - #66DaysofData  in which I will be learning something new from the Data Science field for 66 days, and I will be posting daily topics on my LinkedIn, On my GitHub repository, and on my blog as well.


Stay Curious!  





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