We often see players on the sidelines of a football game inhaling oxygen. Their

Question

We often see players on the sidelines of a football game inhaling oxygen. Their coaches think this will speed their recovery. We might measure recovery from intense exercise as follows: Have a football player run 100 yards three times in quick succession. Then allow three minutes to rest before running 100 yards again. Time the final run. Because players vary greatly in speed, you plan a matched pairs experiment using 25 football players as subjects. Discuss the design of such an experiment to investigate the effect of inhaling oxygen during the final rest period.(Select all that apply. Each player will be put through the sequence once. Each player will be put through the sequence twice. For each player, randomly determine whether to use oxygen on the first or second trial For each player, randomly assign players to use oxygen or to not use oxygen. Allow ample time between trials for full recovery Conduct the trials in succession to avoid time bias

Explanation / Answer

The claim we wish to test is that the oxygen improves the players' recovery time, or they run faster with oxygen than without. Therefore, We set our null hypothesis (h0) to be football players run slower or exhibit no change in recovery time after receiving oxygen therapy during recovery time. The alternative hypothesis (h1) is football players can run faster after receiving oxygen therapy during recovery time.

Now, We only have 25 test subjects, so we don't have a standardized distribution, which requires at least 30 test subjects, so we will be using the t-table distribution chart. we will also need to assign a p-value of say 0.05 (or, that we can guarantee our results with a 5% margin or error.) Have our players run the tests, and record times for each person with and without oxygen therapy.Then we determine the difference in times, by taking the "with oxygen" times and subtracting the "without oxygen" times. we can calculate the mean difference, and then calculate the standard deviation from the mean. Using the t-table and p-value we can calculate whether the difference we have got falls within the standard norm thus supporting the claim of no change, or gives results above or below the normal parameters, thus supporting or negating our null hypothesis. Once we have supported or negated our null hypothesis, only then we can determine if our original claim is upheld.

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