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The central limit theorem fundamentally asserts that as the sample size increases, the sampling distribution of the sample mean will tend toward a normal distribution, regardless of the original distribution of the population from which the samples are drawn. This is significant because it allows statisticians to make inferences about population parameters even when the underlying population distribution is not normal, as long as the sample size is sufficiently large.

This property is essential in statistics because it forms the basis for many statistical techniques, including hypothesis testing and confidence intervals, allowing for robust conclusions based on sample data. The normal approximation becomes increasingly accurate with larger sample sizes, typically around 30 or more.

The other options do not accurately reflect the implications of the central limit theorem. For example, the first option inaccurately suggests that the sample median follows a binomial distribution, which is not a general case for the sampling distribution. The third option erroneously claims that the sampling distribution of any statistic is always skewed; this is not true since the central limit theorem can lead to normality under certain conditions. The fourth option mistakenly suggests that the sampling distribution of the sample mean is always normal, when in fact, it only approaches normality as sample size increases.