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Sunlight Protection Factor

Sunlight Protection Factor. Stephanie Guan. Data. Box Plot of Sunlight Protection Factors. One-sample t-test.

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Sunlight Protection Factor

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  1. Sunlight Protection Factor Stephanie Guan

  2. Data

  3. Box Plot of Sunlight Protection Factors

  4. One-sample t-test Sunlight protection factor is determined by amount of time spent in the sun without damage with the sunscreen divided by the amount of time spent in the sun without the sunscreen without damage. So I divided the during treatment number of minutes of tolerance by the pretreatment number of minutes of tolerance for each of the 13 patients and conducted a one-sample t-test with the results. I felt that a two sample paired test would not accurately reflect what we’re trying to find because we are not trying to find the difference in minutes tolerated but instead the sunlight protection factor. Because the paired test analyzes paired differences, I can’t use that test. I have included the results of the hypothesis test, although I think the question did not ask for them and it is rather worthless because we already know that the sunlight protection factor is not equal to zero. t = 5.5442, df = 12, p-value = 0.0001 95 percent confidence interval: (5.598494 12.847660). mean of sample = 9.223077. The sample averages could vary but a plausible interval of the spf’s is between 5.6 and 12.8. I don’t think that the Wilcoxon signed rank test would have helped us in getting what we want: the confidence interval, because it is a hypothesis test to see if there were treatment effects, so I did not post the results of that on this presentation. I think I’m pretty sure that there is a treatment difference. What we want is a range of values for the spf, provided by the confidence interval.

  5. Results • First, the sample size is small, less than 20, so the results might not have been accurate. • An examination of the box plot (slide 3) of the spf’s for each individual suggests a slightly right-skewed distribution but without outliers. Because this is a small sample, a skewed distribution could adversely affect the accuracy of our results. • Also, I don’t think that the patients were randomly selected, so the t-test might not give us good results. • Because this is not a randomized experiment but an observational study, there might be confounding variables that could account for the spf. Possible confounding variables: the patients who usually take the time to participate might have an interest in testing the quality of the product, i.e. they might be people who suffer from sunburns more often than the general population; because all of the patients have erythropoietic protoporphyria, this disease might affect the outcome, so we probably shouldn’t generalize our results to the general population. In fact, we might not even generalize our results to erythropoietic protoporphyria patients because of such a small, nonrandom sample size.

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