weaknesses
As in all studies, ours too contained some weaknesses. One of these weaknesses includes the fact that not all 200 surveys were returned back to us. Also, because we chose to survey juniors and seniors, we came across many students who were full time Polaris or PSOEP, meaning they did not attend the high school at all through out the day so we were unable to distribute them a survey. Another weakness we had, we didn't come across until we went to calculate our p-value. Due to the fact that on our survey you did not answer the last two questions if you did not have your drivers license, when we entered our data, it was unable to gives us a p-value. This caused us to have to go back and separate our data and use only the data that contained students with their drivers license. We had few values that were greater than 5 which made our Chi-Square test relatively invalid. Response bias could also be a weakness in this study because some students may have claimed to have a clean driving record when in reality, that is not the case. It works the opposite way too. We had one survey claim they were 16 years old, had their drivers license, and had been pulled over 13 times with 7 citations. There is no way that can be accurate because in the state of Ohio, that student would have had their license revoked a long time prior to that.
extrapolation
We feel that our study could be extrapolated to almost any population. Every school district contains both male and female student drivers. which means that there are some of those drivers who receive traffic citations or get pulled over by the police. You could also conduct the same survey for the city of North Olmsted or even the state of Ohio. Granted in those two cases we would be surveying the entire population rather than solely students. People drive almost every where so extrapolating our study would not be difficult at all.
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