When is a sample population curve more likely to resemble the population curve?

A sample population curve is more likely to resemble the population curve when the sample size is greater than 30. This principle is rooted in the Central Limit Theorem, which states that as the sample size increases, the distribution of the sample means will approach a normal distribution, regardless of the shape of the population distribution.

When the sample size exceeds 30, the sample mean's distribution becomes increasingly bell-shaped, which allows for more stable and accurate estimations of the population parameters. This larger sample size helps mitigate the effect of outliers and variability, providing a clearer reflection of the overall population characteristics.

Smaller sample sizes may not capture the diversity of the population adequately, leading to a skewed or inaccurate representation. A wide bell shape might indicate variability in the population data rather than closeness to the population mean, making it less reliable in comparison to a sample size that is larger than 30. Hence, a larger sample size contributes to a more precise and representative sampling distribution.