Answer:
The answer is "Sample size is important when the population is not normally distributed ".
Step-by-step explanation:
The theorem for the central limit indicates that perhaps the sample distribution of means by the sample is close to the confidence interval independent of the underlying population demographics when large samples are derived from every population, with [tex]mean = \mu[/tex] and confidence interval [tex](S.D) = \sigma[/tex]. The bigger the sample, the stronger the approach (typically [tex]n \geq 30[/tex]). The sample is therefore significant unless the population is not typically spread.
