Explain why increasing the sample size tends to result in a smaller sampling error when a sample mean is used to estimate a population mean.

A. The above statement is incorrect, the sample size has no effect on the sampling error.
B. The larger the sample size, the more closely the possible values of \bar{x} cluster around the mean of \bar{x}
C. If the sample size is larger, the possible values of \bar{x} are farther from the mean of \bar{x}

Question

Explain why increasing the sample size tends to result in a smaller sampling error when a sample mean is used to estimate a population mean. 

 A. The above statement is incorrect, the sample size has no effect on the sampling error. 
 B. The larger the sample size, the more closely the possible values of \bar{x} cluster around the mean of \bar{x} 
 C. If the sample size is larger, the possible values of \bar{x} are farther from the mean of \bar{x}

amistre64 · Answer

only one of them is a reasonable option ....

anonymous · Answer

yeah I know Im not sure which one @amistre64

amistre64 · Answer

you dont have to be sure ... which one do you think it would be?  or at least which ones do you think are just plain ignorant.

anonymous · Answer

IM kind of leaning towards B? @amistre64

amistre64 · Answer

me too :)

amistre64 · Answer

spose we have a population of 100

the closer we are to 100, the better chance of being closer to the population mean