Central limit theorem >When sampling from a normally distributed population with mean u the distribution of the sample mean will be normal with mean u
➢ When sampling from a normally distributed population with mean μ, the distribution of the sample mean will be normal with mean μ Central limit Theorem
=10 n=16 0==2.5 =50 X 2=50 opulation Sampling distribution distribution
= 50 =10 X Population distribution n = 4 Sampling distribution X n =16 = 5 x = 50 x = 2.5 x
Central limit Theorem When sampling from a nonnormally distributed population with mean u the distribution of the sample mean will be approximately normal with mean u as long as n is larger enough(n>50)
➢ When sampling from a nonnormally distributed population with mean μ, the distribution of the sample mean will be approximately normal with mean μ as long as n is larger enough (n>50). Central limit Theorem
oX X
x n = x = X
Standard error(se)can be used to assess sampling error of mean Although sampling error is inevitable, it can be calculated accurately
Standard error (SE) can be used to assess sampling error of mean. Although sampling error is inevitable, it can be calculated accurately