Which of the following is an example of a dependent sample suitable for analysis with the $t$-test?

When testing the difference of means for paired data, which of the following is the correct null hypothesis?

Which of the following scenarios is most likely to involve dependent samples ($\mathrm{matched} \mathrm{pairs}$)?

Two samples are $————————————$ if the sample values are paired.

Which of the following situations is a matched pairs $t$ test not an appropriate way to analyze?

"[DATA] Does Octane Matter? Octane is a measure of how much fuel can be compressed before it spontaneously ignites. Some people believe that higher-octane fuels result in better gas mileage for their cars. To test this claim, a researcher randomly selected 11 individuals (and their cars) to participate in the study. Each participant received 10 gallons of gas and drove his or her car on a closed course that simulated both city and highway driving. The number of miles driven until the car ran out of gas was recorded. A coin flip was used to determine whether the car was filled up with 87-octane or 92-octane fuel first, and the driver did not know which type of fuel was in the tank. The results are in the following table:

d. The differences are computed as “92 octane minus 87 octane.” The normal probability plot of the differences is shown in the next column. The correlation between the differenced data and the expected z-scores is 0.966. Is there reason to believe that the differences are normally distributed? Conclude that the differences can be normally distributed even though the original data are not.