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Does the amount of sleep a person gets affect their overall happiness?

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We went to the local high school and randomly selected 100 students. 50 males and 50 females. 20 males and females were placed in Group A, and 20 were placed in Group B. 10 were reserved as a control group.

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The first 5 male names, and the first 5 female names drawn were set as the control group. They were told to follow their sleep patterns as normal but record the hours they slept and to record their happiness level on a scale of 1-10 upon waking up and again right before bed. We do not have a placebo as that kind of observation is not possible for our experiment type.

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With a sample size of 100 students, we have a margin of error at 10%. This margin of error is relatively low which means the results of our experiment should be a close and accurate representation of the population.

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Our response variable is the levels of happiness recorded each day by the participants over the 2 week study.

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Every student name was placed into their gender corresponding bin. 50 names from each bin were then drawn at random. Lurking variables may include, medications and sleep disorders, activity level throughout the day, home and personal life, and emotional state prior to the experiment.

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In Group A, 20 participants were subjected to 5 hours of sleep each night and in Group B, 20 participants were subject to 10 hours of sleep each night. Each person was to sleep their assigned amount of hours every night for two weeks and record their happiness level on a scale of 1-10 each morning when they woke up and each night before bed.

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Our explanatory variable was the treatments Group A and Group B received; 5 hours of sleep for Group A and 10 hours of sleep for Group B.

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We selected the High School but the students were chosen at random by a simple random sample lottery. All the male students names were placed in one bin and all the female in another and 50 were selected from each bin.

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We cannot blind our experiment subjects because they know how many hours of sleep that they're getting and how happy they are and they have to report that data to us. A double-blind was not appropriate for this experiment either because our researchers knew who was in what group and the numbers they were reporting in relation to their treatment.

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We are using a crossover design by going to 3 different high schools during 3 different time periods with a different subject pool each time. This will allow us to have more data and results which will in turn decrease of margin of error and be sure of the results we are receiving.

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In order to confirm our results we will preform the experiment a second and third time at a different high schoola, and with a different sample of subjects.

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The outcome of the experiment showed that happiness levels are affected by the amount of sleep a person gets and that with about 10 hours of sleep, a person's happiness tends to generally increase over time. These results were sent to the High Schools involved in the experiments and published in several research magazines targeting psychiatry and sleep medicine.

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After 14 days, Group A, the group getting only 5 hours of sleep, reported much lower averages than Group B who was getting 10 hours. Both groups started around a score of 5 for their happiness levels, but A continued to decrease and B continued to increase leading our researches to form the conclusion that sleep does affect happiness levels.

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A team of 6 researchers, 2 for each group, were paid 25 dollars a day to obtain the data reports from each student. 2 additional researchers were hired to compile and calculate all the data at 12.50 an hour. 6 researchers being paid $25 dollars per day for 14 days calculates to $2,100. It took the additional researches 4 hours to compile and calculate the data. So 2 researchers working at $12.50 an hour for four hours calculates to $100. So altogether, the project cost a total of $2, 200.

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These results are statistically significant because the experiment was conducted using a number scale and averages that were trended over time. By doing this, a statistical outcome is given that shows actually values rather than an approximation or guess with no data to back it up.