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Introduction to biostatistics
Types of data
Fisher's exact test
Kaplan-Meier survival analysis
Mann-Whitney U test
Spearman's rank correlation coefficient
Type I and type II errors
Hypothesis testing: One-tailed and two-tailed tests
Methods of regression analysis
Repeated measures ANOVA
Mean, median, and mode
Normal distribution and z-scores
Range, variance, and standard deviation
Standard error of the mean (Central limit theorem)
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false-positive VDRL p. 146
Let’s say that you’re trying to figure out if a certain medication, Medication A, lowers blood pressure better than the currently prescribed medication, Medication B. So you find 100 people with high blood pressure and give 50 of them Medication A and 50 of them Medication B, and after 6 months see which group has lower mean or average blood pressure.
For this study we would make two hypotheses.
The first hypothesis is called the null hypothesis, and it basically says there’s no difference between two variables that you care about.
For example, our null hypothesis would state that there’s no difference between the mean blood pressure after the 6 month study period, for the group that takes Medication A compared to the mean blood pressure for the group that takes Medication B.
In other words, that there’s no relationship between medication type and blood pressure.
On the other hand, the alternate hypothesis would state that there is a difference between the mean blood pressure for the group that takes Medication A compared to the mean blood pressure for the group that takes Medication B.
Again, in other words, that there is a relationship between medication type and blood pressure.
In theory, there are four possible conclusions that can come from this study, and we can organize them in a 2 by 2 table, where the true relationship between medication and blood pressure is on top, and the study conclusions are on the side.
When a study doesn’t see a relationship between medication and blood pressure, represented here as an arrow with a red cross, and there really isn’t one, then this is called a true negative.
When the study finds that there is a relationship, represented on our table by a green arrow, between medication and blood pressure, and there really is one, then this is a true positive.
Similarly, when the study concludes that there is a relationship between medication and blood pressure but there really is no difference - this is a false positive, also called a type I error.
Two types of errors can occur in statistics and hypothesis testing. These are Type I and Type II errors. Type I error, also known as a false positive, occurs when a researcher rejects a null hypothesis that is actually true. In other words, the researcher concludes that there is a significant effect or relationship when there really isn't. On the other hand, type II error, which is also known as a false negative, occurs when a researcher fails to reject a null hypothesis that is actually false. In other words, the researcher concludes that there is no significant effect or relationship when there really is.
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