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Probability Theory
In this case study, I collected information about the number of text messages I receive each day. Although I have multiple cell phones, only one of them is dedicated to personal use, so I documented the number of text messages that I received only on that cell phone. Moreover, I only tracked the number of actual text messages that I received, not the number of instant messages, emails, or other forms of electronic communication that I receive during the day. I divided my data into a.m. And p.m. divisions, and collected the data over a ten-day period. My cell phone provider allows me to track text messages received and records the time at which they are received, so that I did not have to track the data on a daily basis. Instead, I could look back over the data that I had and determine how many text messages were received during each 12-hour block.
Before beginning the data collection, I believed that I would receive far more messages in the latter-half of the day than in the early half of the day, because I tend to be a late riser. However, I forgot to account for the fact that many of my acquaintances and I are up well past midnight. As a result, it appeared that much of my heaviest texting traffic occurred in the hour or two after midnight. Therefore, my a.m. And p.m. texting numbers appear to be somewhat equal. Were I to recreate the experiment, I would divide the information into different 12-hour segments, ending the second segment at the approximate time that I go to bed, to account for the difference between my schedule and the schedule of the average person who wakes up around 7 a.m. And goes to bed around 11 p.m.
Results
Day
12 AM-11:59AM
12:00 PM- 11:59 PM
1
24
17
2
19
38
3
36
12
4
14
23
5
15
19
6
45
11
7
18
15
8
13
29
9
31
56
10
10
17
Text messages received over a 10-day period.
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