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An exercise physiologist is interested in the effect of exercise on heart rate.

She has the subjects walk slowly for $5\text{ minutes}$ and records their heart rates. Then she has the subjects run vigorously for $5\text{ minutes}$ and records their heart rates again.

The differences are recorded (heart rate after running - heart rate before running), with results below:

One-Sample $T$: Differences

Test of $\mu = 0$ vs. $\mu \neq 0$

Variable N Mean StDev SE Mean 95% CI T P
Differences 8 59.88 10.36 3.66 (51.22,68.53) 16.35 0.000

Why could a confidence interval be more useful in this situation than a hypothesis test?


A confidence interval meets the conditions necessary while a hypothesis test does not.


A confidence interval will have more power than a hypothesis test.


A confidence interval will have a lower margin of error than a hypothesis test might.


A confidence interval gives more detailed information than a hypothesis test does.


There is a lower chance of a type II error in a confidence interval than in a hypothesis test.

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