STATISTICAL TECHNIQUES AND TOOLS
SAMPLING DISTRIBUTION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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the value of the statistic from any sample is NOT equal to the parameter
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The value of the statistic from any sample IS equal to the parameter
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The mean of the sampling distribution is NOT equal to the true value of the parameter
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The mean of the sampling distribution IS equal to the vali
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Detailed explanation-1: -Biased estimator A statistic used to estimate a parameter is a biased estimator if the mean of its sampling distribution is not equal to the true value of the parameter being estimated. Central limit theorem (CLT) Draw an SRS of size n from any population with mean and finite standard deviation .
Detailed explanation-2: -The mean of the sampling distribution of the sample mean will always be the same as the mean of the original non-normal distribution. In other words, the sample mean is equal to the population mean. where is population standard deviation and n is sample size.
Detailed explanation-3: -In order for an estimator to be unbiased, its expected value must exactly equal the value of the population parameter. The bias of an estimator is the difference between the expected value of the estimator and the actual parameter value. Thus, if this difference is non-zero, then the estimator has bias.