In the provided SQL solutions, I calculated averages using the "average_units_sold" column. An average represents the typical value in a dataset, obtained by dividing the sum of all values by the total number of values. In this context, it reflects the typical number of units sold for each product. The COALESCE function handles null values by replacing them with zeros to ensure accurate calculations.
There are various approaches to calculating averages, including arithmetic mean, median, mode, and weighted averages. I chose to calculate the arithmetic mean because it's the most commonly used method and provides a straightforward representation of the central tendency of the data. Additionally, using the percentile_cont function helps mitigate the impact of outliers on the average, providing a more robust measure of central tendency.
I choose this summary for its clarity and simplicity, making it easy for every player to understand the concept of averages and the specific approach used in the SQL solutions provided. It highlights the importance of accurately representing data trends, especially in business decision-making contexts