Answer:
Explanation:
Many data sets naturally fit a non normal model. For example, the number of accidents tends to fit a Poisson distribution and lifetimes of products usually fit a Weibull distribution. However, there may be times when your data is supposed to fit a normal distribution, but doesn’t. If this is a case, it’s time to take a close look at your data
The central limit theorem allows us to apply normal calculations to non-normal distributions
The central limit theorem states that if you have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement , then the distribution of the sample means will be approximately normally distributed.
Central limit theorem is applicable for a sufficiently large sample sizes (n ≥ 30). The formula for central limit theorem can be stated as follows: μ x ― = μ a n d. σ x ― = σ n.
It must be sampled randomly. Samples should be independent of each other. One sample should not influence the other samples. Sample size should be not more than 10% of the population when sampling is done without replacement.
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