July 10, 2016 Drug Research Update
Before running tests on the data, we were looking for a correlation of genders to drinking and smoking. After running a few statistical tests on a set of data provided from a government source, we concluded that there is a significant correlation between genders in terms of drinking and smoking cigarettes. The tests we ran are T.tests and Chi squared tests, both which show significant correlation between genders in terms of alcohol and drugs.
Drank Alcohol
|
Haven't Drank Alcohol Before
| |
Male
|
7,424
|
4,909
|
Female
|
7,518
|
4,558
|
data: drank.table
X-squared = 10.814, df = 1, p-value = 0.001007
The p-value is approximately 0.01, for this data. This means that there is a significant correlation between gender and patterns of alcohol consumption.
Smoked Before
|
Haven't Smoked Before
| |
Male
|
6,787
|
20,080
|
Female
|
6,914
|
22,521
|
data: smoked.table
X-squared = 23.869, df = 1, p-value = 1.031e-06
The p-value from this test was 1.031 * 10-6, indicating that there is a significant difference between genders in terms of prior alcohol consumption. The data show that females are significantly more likely than men to have consumed alcohol while underage.
The p-value is a measure of the significance of a given set of data. The p-value tells the researcher the probability of the data given his or her hypothesis. Given the hypothesis, our p-value shows the probability that our data shows correlation.
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ReplyDeleteLooking at your data, it seems that females only drink more than men by about 100 people. Is this significant enough to say that females are more likely to have consumed alcohol while underage? Also, are your tables measuring underage drinking, if so, you might want to put that in the title.
ReplyDeleteThere is an explanation for alcohol consumption under the smoking data. What is the outcome of the smoking data?
ReplyDeleteLooking at the tables, there does not seem to be that much of a difference between the female and male numbers. So why is the p-value so tiny?
ReplyDeleteInteresting results, it would be interesting to see if the difference in means would still be as different as it is if the sample was of all the teens in the country
ReplyDeleteIs the second p-value given describing the correlation between genders and alcohol consumption or in smoking? Additionally, for the first p-value, what is the significant correlation between genders and patterns of alcohol consumption being described?
ReplyDeleteIt is interesting to see that there wasn't much of a difference between males and females drinking and smoking. Because of this I am wondering why you got such a small p-value.
ReplyDeleteIn your introduction, you said that you used both t-tests and chi squared tests, but I don't see any results based on a chi squared test. What did you test with chi squared?
ReplyDeleteI don't think that the p-values you have are what you've interpreted them to be. The p-values you have seem rather smaller in comparison to the closeness of your data.
ReplyDeleteI don't think that the p-values you have are what you've interpreted them to be. The p-values you have seem rather smaller in comparison to the closeness of your data.
ReplyDeleteI think that we need to investigate a little more what our p-values mean, concisely put what they show. Since we have such a large data set, random chance becomes less and less of a problem so instead of recalculating our p-values we should look into what they mean.
ReplyDelete