UGC NET COMMERCE November 2017(Paper-II) Q20
- Option : C
- Explanation : The parameters of the sampling distribution
are related to the parameters of the distribution
of data. The means of the two distributions
are equal to the same value. This will always
be the case for any unbiased estimate. An
unbiased estimate is equal to the population’s
parameter, on the average. This is reflected in
the sampling distribution by having the mean
equal to the parameter that is being estimated
(i.e., mean of the distribution of data).
The variances and standard deviations of the sampling distribution and distribution of data are related, but not equal to the same value. The sampling distribution is less dispersed than is the distribution of data. The reason for this is that the impact of extreme data values in a sample is offset by less extreme data values. Thus, estimates of the mean have less variation than do the data. How much less, depends on the number of observations in the sample. The greater the number of observations, the less variable the estimates. To emphasize the difference in dispersion between the sampling distribution and the distribution of data, the standard deviation of the sampling distribution is usually called the standard error.
