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An office manager wonders if her employees put in more hours of work when free bagels are delivered every day.

The manager randomly selects $35\text{ workers}$ to whom bagels will be delivered and compares the number of hours worked by these individuals to another randomly selected group of $35\text{ workers}$ who do not receive bagels.

The computer output is below.

Two-Sample $T$-Test and Confidence Interval (CI)

Sample N Mean StDev SE Mean
Bagels 35 40.56 2.45 0.41
No Bagels 35 39.98 2.23 0.38

Difference = $\mu(1)-\mu(2)$

Estimate for difference: $0.580$

$95\%$ CI for difference: $(-0.538, 1.698)$

$T$-test of difference = $0$ (vs. not =): $T$-value = $1.04$
$P$-value = $0.304$
DF = $67$

What practical conclusion can be made based off of this data?

A

There is strong evidence that bagels do not cause workers to put in more hours.

B

There is strong evidence that bagels do cause workers to put in more hours.

C

The evidence is inconclusive, so no conclusions whatsoever can be drawn.

D

A moderate correlation appears between bagels and worker hours.

E

Based off this experiment, there is insufficient evidence that bagels cause an increase in worker hours.

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