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Hi Lauren, your deduction is correct! The first part of the central limit theorem is demonstrated by showing a normal distribution after the sampling of the TPCP data. A normal distribution can be identified by the bell-shaped curve in the histogram. The data looks more symmetrically distributed compared to the TPCP histogram we first see. When the grand mean, the average of the 1000 samples, is in close approximation with the original mean, it proves the second part of the central limit theorem, which you are also right about! | also am having a little bit of trouble understanding these new concepts, but this is how | understand it. | hope it helps! Hi Reice, your 1000 sample means histogram definitely shows a normal distribution with its bell-shaped curve. Although it would have been nice to look at the skewed histogram in comparison to the 1000 means histogram, it can be somewhat visualized with the TPCP data frame. The grand mean of 485.95 is indeed close to the TPCP mean of 487.54, which does validate the second part of central limit theorem. Based off of the 1000 means histogram and data it provides, it allows us to draw better conclusions compared to the skewed TPCP