Interpreting a confidence interval for estimating a population mean

This week's discussion surrounds another example of how an industry used statistics to make important analytics decisions about how it best served its customers:

Data collected among parents spent on their children's birthday parties (under 10 years of age) from a random sample gathered in 1975 found to have a mean expense of $100 including gifts.

In a more recent sampling, however, the mean expense has increased considerably, to $275 including gifts.

Now that we have the data, we move on to the decision-making of analytics. What real-world implications might these data have for toy makers? Party suppliers? Children? Parents?

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