The sampling error is inversely proportional to the square root of the sample size.
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It is reduced.
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.
Margin of error, level of significance and level of power are all elements that will affect the determination of sample size.
The standard error is the standard deviation divided by the square root of the sample size.
A sample of size 100.