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Why the central limit theorem tells us that it is OK to use the normal distribution to determine probabilities of x?
Wiki User
∙ 10y ago
The Central Limit Theorem (CLT) says no such thing! In fact, it states the exact opposite.
The CLT sets out the conditions under which you may use the normal distribution as an approximation to determine the probabilities of a variable X. If those conditions are not met then it is NOT OK to use the normal distribution.
Wiki User
∙ 10y ago
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What is the definition of central limit theorem?
The central limit theorem basically states that as the sample size gets large enough, the sampling distribution becomes more normal regardless of the population distribution.
How do you know x bar and R charts follow normal distribution?
Central Limit Theorem
Why does a binomial distribution become more skewed as n increases?
As n increases, the distribution becomes more normal per the central limit theorem.
Why is the central limit theorem an important idea for dealing with a population not normally distributed?
According to the Central Limit Theorem, even if a variable has an underlying distribution which is not Normal, the means of random samples from the population will be normally distributed with the population mean as its mean.
What is the difference between Gamma distribution and Central limit theorem?
There is abig difference between them..gamma is a distribution but central limit theorm is just like a method or technique u use to approximate gamma to another distriution which is normal....stupid
What does the central limit theorem say about the shape of the sampling distribution of?
The central limit theorem can be used to determine the shape of a sampling distribution in which of the following scenarios?
What is the definition of central limit theorem?
The central limit theorem basically states that as the sample size gets large enough, the sampling distribution becomes more normal regardless of the population distribution.
Can you use the normal distribution to approximate the binomial distribution. Give reason?
Yes, and the justification comes from the Central Limit Theorem.
The mean of a sampling distribution is equal to the mean of the underlying population?
This is the Central Limit Theorem.
Bayes theorem uses Prior probabilities with additional information to compute posterior probabilities?
False
Does the central limit theorem states that as sample size increases the population distribution more closely approximates a normal distribution?
Yes.
What name do you give to the standard deviation of the sampling distribution of sample means?
the central limit theorem
How do you know x bar and R charts follow normal distribution?
Central Limit Theorem
Why is central limit theorem important?
The central limit theorem is one of two fundamental theories of probability. It's very important because its the reason a great number of statistical procedures work. The theorem states the distribution of an average has the tendency to be normal, even when it turns out that the distribution from which the average is calculated is definitely non-normal.
The Central Limit Theorem is important in statistics because?
According to the central limit theorem, as the sample size gets larger, the sampling distribution becomes closer to the Gaussian (Normal) regardless of the distribution of the original population. Equivalently, the sampling distribution of the means of a number of samples also becomes closer to the Gaussian distribution. This is the justification for using the Gaussian distribution for statistical procedures such as estimation and hypothesis testing.
Why does a binomial distribution become more skewed as n increases?
As n increases, the distribution becomes more normal per the central limit theorem.
Why Central Limit Theorem does not work for sample max?
Because other than in a degenerate case, the maximum of a set of observations is not at its centre! And the theorem concerns the distribution of estimates of the central value - as the name might suggest!
