An analyst is interested on comparing two audiences: men, ages 25-34 and men, ages 35-44. The brand wants to know if the older male customers spend more money on average than the younger male customers. The analyst collected random samples of 250 older customers and 220 younger customers and analyzed their shopping baskets. On average, younger men spend $102.23, and older men spend $86.46. Additional statistics are shown below. 
A . I had something like this in a mock-since the average spend for younger men is higher, but with a 90% confidence level, unless the difference is statistically significant, you can't claim there's a real difference. The sample means alone aren’t enough without supporting stats (like p-value). Pretty sure that's what they're testing here. Open to corrections if I missed something in the stats!
Option D. If the question had asked for a 10% confidence level instead of 90%, would that change which answer fits best?
Pretty common to see this in practice sets. D matches what you'd get from r squared (roughly 36%), and exam questions sometimes round. Official guide reviews the stats basics like this!
What should the company test using experimental design to improve efficiency in number of
exposures?
What interpretation should be made from the output?
How many of these data points are likely to skew the findings of this analysis?