1. The groups were independent because the men and women participating in the experiment did not share any relationships and their quality of life tests were totally independent of one another. 2. The variable described by this number is mental health. Since the P value is .002 and the alpha is .05, the difference can cause the null hypothesis to be rejected. 3. The T-Value is significant because the P value is .049 which is the closest to the alpa which is .05. Since this is one of the values that allows the selected hypothesis to be correct the significance is support to the performed experiment. 4. The mental health variable seems to have the biggest difference between men and woman. The t-score shows a -3.15 difference and although this seems huge the P-score diminishes the amount of certainty thee data provides since it is significantly lower than the alpha. 5. The t=-2.54 has a smaller P-Score. This shows that the physical component score has a lot more to do with the quality of life function within men and woman. 6. A type I error is when a true null hypothesis is rejected. Certain variables that have a P-score that is lower than the alpha would need multiple trials to ensure that the null hypothesis should be rejected since outliers are a commonality and multiple trials ensure that data is correct. 7. A Bonferroni procedure would be helpful since it would counteract the problem of multiple variables the experiment does conclude. Since the different variables have p-scores and t-scores differencing across the board such a procedure would help minimize the error in the patients scores. 8. 0.05/9 = 0.0056 The calculation would the alpha divided by the number of t-tests which would be .05 divided by 9 which would come out to .0056. 9. Since there was a reduced amount of men participating in the family variable, 3 less, there was multiple df values due to this. 10. The data shows that their physical level is only 50%...
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