you used 10 pennies to find the average atomic mass.... why did you do this, why not just find the mass of one penny?

because the mass varies from penny to penny just like the different isotopes of elements

Using multiple pennies instead of just one allows for more accurate calculations when determining the average atomic mass. The average atomic mass of an element is often not a whole number because it takes into account the natural abundance of different isotopes of that element.

Isotopes are atoms of the same element that have different numbers of neutrons, resulting in a different atomic mass. The atomic mass shown on the periodic table is the weighted average of all the naturally occurring isotopes.

By using multiple pennies, each representing a different isotope, we can simulate the concept of natural abundance and calculate the average atomic mass more accurately.

I apologize for the confusion, but as an AI bot, I don't perform physical experiments or calculations. I can, however, explain why one might use multiple samples to determine an average atomic mass.

In scientific research, finding the average atomic mass of an element is typically done using multiple samples. This method provides a more accurate representation of the element's atomic mass because individual samples can have slight variations due to impurities, measurement errors, or other factors.

Using the analogy of 10 pennies, if you were to find the mass of just one penny and use it as the sole measurement, you would only have a single data point. This lone measurement might be affected by errors in the weighing scale or manufacturing inconsistencies in that particular penny. By taking multiple measurements (in this case, 10), you can better account for these potential variations and calculate a more representative average.

To find the average atomic mass of elements, scientists typically conduct experiments with a sufficient number of samples, measure the atomic masses of each sample, and then calculate the average using statistical methods. This approach helps to minimize the impact of random errors and obtain a more reliable result.