\( \begin{array}{l}\text { As the sample size becomes larger, the sampling distribution of the sample mean approaches a } \\ \text { distribution. } \\ \text { O Chi-square } \\ \text { O Binomial } \\ \text { O Normal } \\ \text { Poisson }\end{array} \)

As the sample size increases, thanks to the Central Limit Theorem, the sampling distribution of the sample mean tends to resemble a normal distribution, regardless of the shape of the original population distribution. This means that even if you're pulling from a skewed or odd-shaped dataset, as your sample gets bigger, the average will become more bell-shaped! In real-world application, this phenomenon is crucial for statisticians and researchers. It allows for the use of normal distribution properties to make inferences about population parameters, conduct hypothesis testing, and create confidence intervals, making statistical analysis much more robust and reliable even with smaller samples from non-normal populations!