Yes, and the justification comes from the Central Limit Theorem.
This is the Central Limit Theorem.
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 states that the sampling distribution of the sample means approaches a normal distribution as the sample size gets larger — no matter what the shape of the population distribution. This fact holds especially true for sample sizes over 30.
According to the Central Limit Theorem, the mean of a sufficiently large number of independent random variables which have a well defined mean and a well defined variance, is approximately normally distributed.The necessary requirements are shown in bold.According to the Central Limit Theorem, the mean of a sufficiently large number of independent random variables which have a well defined mean and a well defined variance, is approximately normally distributed.The necessary requirements are shown in bold.According to the Central Limit Theorem, the mean of a sufficiently large number of independent random variables which have a well defined mean and a well defined variance, is approximately normally distributed.The necessary requirements are shown in bold.According to the Central Limit Theorem, the mean of a sufficiently large number of independent random variables which have a well defined mean and a well defined variance, is approximately normally distributed.The necessary requirements are shown in bold.
The Central Limit THeorem say that the sampling distribution of .. is ... It would help if you read your question before posting it.
The Central Limit Theorem (abbreviated as CLT) states that random variables that are independent of each other will have a normally distributed mean.
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.
Yes, and the justification comes from the Central Limit Theorem.
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.
This is the Central Limit Theorem.
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.
You use statistical techniques, and the 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
False
the central limit theorem
Central Limit Theorem